Analytics software sponsored by the National Institute of Health and the Air Force Office of Scientific Research
SEATTLE - September 6, 2007 -
(PRWEB) — Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today the immediate release of three time series analysis software packages via Insightful’s Comprehensive S-PLUS® Archive Network (CSAN). The development of these software packages was supported by the National Institutes of Health under grant number R44LM007146, entitled "Fractal Modeling of Biomedical Time Series in S-PLUS," and by the Air Force Office of Scientific Research under contract number F49620-99-C-0039, entitled “Wavelet-Based Analysis and Software for Multi-Scale Fractal Processes.”
“We are pleased to have developed and released the first S-PLUS packages for fractal time series modeling and analysis,” said Dr. Jill R. Goldschneider, director of research of Insightful. “We believe these S-PLUS packages will contribute to research in advanced statistical computing around the world, reinforcing Insightful’s leadership in the development of cutting edge statistics.”
Simply put, the term "fractal" means that certain statistical measures of data are invariant upon rescaling the data. However, the measures are quite different for stochastic and deterministic models (e.g., invariance in distributional properties in the former and invariance in space-filling properties in the latter). Researchers have used stochastic fractal models to interpret observed data from real-world systems such as cardio-dynamics (heart rate variability), turbulence, atmospheric and climate changes, atomic clock deviations, sea ice measurements and blood flow.
Insightful has released the following time series packages via CSAN at http://csan.insightful.com:
* FRACTAL: stochastic fractal time series and nonlinear modeling
* WMTSA: wavelet methods for time series analysis
* SAPA: spectral analysis for physical applications
Funding from the awards was recorded in the company’s financial statements as a reduction to research and development expense. The contents of the results of the grants are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health or the Air Force Office of Scientific Research.
Statistical analysis software
Thursday, September 25, 2008
Insightful Corporation Releases S-PLUS® 8 Enterprise Server
New server platform provides improved scalability, reliability and compliance with IT standards for deployment of analytics applications across the enterprise
SEATTLE - September 25, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today the immediate availability of S-PLUS® 8 Enterprise Server, the latest release of Insightful’s software platform for statistical data analysis and predictive analytics.
S-PLUS 8 Enterprise Server provides organizations with the ability to deploy predictive analytics across the enterprise for improved communication and decision-making. Insightful delivers the knowledge to act immediately and decisively, assisting customers in improving operations and enhancing their competitive advantage.
KEY FEATURES IN S-PLUS 8 ENTERPRISE SERVER
* Greater Scalability: The server architecture allows for robust and efficient processing of analytic jobs including load balancing across server clusters. Synchronous and asynchronous access, queuing and scheduling of jobs provides greater flexibility for better performance during peak processing times. Support for 64 bit platforms and out-of-memory processing allow for greater volumes of data to be analyzed faster than with previous versions.
* Improved Security: Single sign-on or pass through authentication through either LDAP or Microsoft® Active Directory® is supported. Encryption is supported through HTTPS or SSL protocols.
* Compliance with IT Standards: Integration and extension of the server is supported through a Services Oriented Architecture including documented C#, .Net, Java, JavaScript, HTTP/URL API’s and rich client examples.
S-PLUS 8 ENTERPRISE SERVER: CONTINUING EVOLUTION FOR ENTERPRISE DEPLOYMENT
S-PLUS 8 Enterprise Server is specifically designed to support the enterprise wide deployment and integration of analytic applications and reporting. The key benefits provided to customers are:
* Improved communication: Results and measurements can be presented in easy-to-understand graphic displays. Decision makers get all of the information they need in an easy-to-understand format for full comprehension of the data being analyzed.
* Timely, fact-based decisions: Rapid response to queries and desktop delivery of results through a web-based portal or custom rich client integration provides decision makers fast and statistically rigorous results upon which to make vital choices for the health of their business.
* Low total cost of ownership: Perpetual licensing coupled with support for popular IT deployment standards make maintenance of the S-PLUS 8 Enterprise Server a much more cost effective alternative to competitive systems.
AVAILABILITY/PLATFORMS
S-PLUS 8 Enterprise Server is available today for the following server environments:
* 32 Bit: Windows, Solaris 8/9/10
* 32 Bit & 64 Bit: Red Hat Linux 3,4, SUSE Linux 10
* In addition, 64 Bit: HP-UX 11i. IBM AIX 5.x will be available later this year.
“We are very pleased to announce the release of S-PLUS 8 Enterprise Server,” said Jeff Coombs, CEO of Insightful. “We are committed to a process of continuous improvement in performance and reliability of our production platform in support of our customers’ enterprise deployment of predictive analytics and reporting applications.”
Statistical analysis software
SEATTLE - September 25, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today the immediate availability of S-PLUS® 8 Enterprise Server, the latest release of Insightful’s software platform for statistical data analysis and predictive analytics.
S-PLUS 8 Enterprise Server provides organizations with the ability to deploy predictive analytics across the enterprise for improved communication and decision-making. Insightful delivers the knowledge to act immediately and decisively, assisting customers in improving operations and enhancing their competitive advantage.
KEY FEATURES IN S-PLUS 8 ENTERPRISE SERVER
* Greater Scalability: The server architecture allows for robust and efficient processing of analytic jobs including load balancing across server clusters. Synchronous and asynchronous access, queuing and scheduling of jobs provides greater flexibility for better performance during peak processing times. Support for 64 bit platforms and out-of-memory processing allow for greater volumes of data to be analyzed faster than with previous versions.
* Improved Security: Single sign-on or pass through authentication through either LDAP or Microsoft® Active Directory® is supported. Encryption is supported through HTTPS or SSL protocols.
* Compliance with IT Standards: Integration and extension of the server is supported through a Services Oriented Architecture including documented C#, .Net, Java, JavaScript, HTTP/URL API’s and rich client examples.
S-PLUS 8 ENTERPRISE SERVER: CONTINUING EVOLUTION FOR ENTERPRISE DEPLOYMENT
S-PLUS 8 Enterprise Server is specifically designed to support the enterprise wide deployment and integration of analytic applications and reporting. The key benefits provided to customers are:
* Improved communication: Results and measurements can be presented in easy-to-understand graphic displays. Decision makers get all of the information they need in an easy-to-understand format for full comprehension of the data being analyzed.
* Timely, fact-based decisions: Rapid response to queries and desktop delivery of results through a web-based portal or custom rich client integration provides decision makers fast and statistically rigorous results upon which to make vital choices for the health of their business.
* Low total cost of ownership: Perpetual licensing coupled with support for popular IT deployment standards make maintenance of the S-PLUS 8 Enterprise Server a much more cost effective alternative to competitive systems.
AVAILABILITY/PLATFORMS
S-PLUS 8 Enterprise Server is available today for the following server environments:
* 32 Bit: Windows, Solaris 8/9/10
* 32 Bit & 64 Bit: Red Hat Linux 3,4, SUSE Linux 10
* In addition, 64 Bit: HP-UX 11i. IBM AIX 5.x will be available later this year.
“We are very pleased to announce the release of S-PLUS 8 Enterprise Server,” said Jeff Coombs, CEO of Insightful. “We are committed to a process of continuous improvement in performance and reliability of our production platform in support of our customers’ enterprise deployment of predictive analytics and reporting applications.”
Statistical analysis software
Insightful Corporation Releases S+FinMetrics 3.0
New release of econometrics library provides market leading time-series modeling, asset valuation, and financial analytics
SEATTLE - September 26, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today the immediate availability of S+FinMetrics® 3.0 for S-PLUS® 8.0, the latest release of Insightful’s software library for advanced financial data analysis.
S+FinMetrics 3.0 provides quantitative analysts and developers with a comprehensive set of financial analytics. Included in the library are vital additions and enhanced features including:
* State-space models and Kalman filter tools
* High frequency functionality
* Option pricing
* Fixed Income calculations
“S+FinMetrics 3.0 is a significant expansion in functionality over the previous version,” said Dr. Eric Zivot, Professor and Gary Waterman Distinguished Scholar in the Economics Department and an Adjunct Professor of Finance in the Business School at the University of Washington. “The specific state space modeling tools for credit risk and option pricing and fixed income pricing functions have greatly enhanced the module, making it a better and more comprehensive collection than ever before.”
CUSTOMER BENEFITS
S+FinMetrics 3.0 combined with S-PLUS 8.0 provides a modeling environment that allows quantitative analysts to explore scenarios and determine results faster. Graphical reporting enables results to be communicated clearly across all audience types. Faster results and clearer communications can assist companies in developing better financial products and responding to changes in the marketplace quickly, in order to maximize profit while minimizing risk.
“The release of S+FinMetrics 3.0 is a breakthrough in providing scalable commercial deployment for the latest and most advanced econometric models,” said Jeff Coombs, president and CEO of Insightful. “We are committed to bringing our customers competitive advantage in the financial markets through the deployment of advanced analytics across the enterprise.”
Statistical analysis software
SEATTLE - September 26, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today the immediate availability of S+FinMetrics® 3.0 for S-PLUS® 8.0, the latest release of Insightful’s software library for advanced financial data analysis.
S+FinMetrics 3.0 provides quantitative analysts and developers with a comprehensive set of financial analytics. Included in the library are vital additions and enhanced features including:
* State-space models and Kalman filter tools
* High frequency functionality
* Option pricing
* Fixed Income calculations
“S+FinMetrics 3.0 is a significant expansion in functionality over the previous version,” said Dr. Eric Zivot, Professor and Gary Waterman Distinguished Scholar in the Economics Department and an Adjunct Professor of Finance in the Business School at the University of Washington. “The specific state space modeling tools for credit risk and option pricing and fixed income pricing functions have greatly enhanced the module, making it a better and more comprehensive collection than ever before.”
CUSTOMER BENEFITS
S+FinMetrics 3.0 combined with S-PLUS 8.0 provides a modeling environment that allows quantitative analysts to explore scenarios and determine results faster. Graphical reporting enables results to be communicated clearly across all audience types. Faster results and clearer communications can assist companies in developing better financial products and responding to changes in the marketplace quickly, in order to maximize profit while minimizing risk.
“The release of S+FinMetrics 3.0 is a breakthrough in providing scalable commercial deployment for the latest and most advanced econometric models,” said Jeff Coombs, president and CEO of Insightful. “We are committed to bringing our customers competitive advantage in the financial markets through the deployment of advanced analytics across the enterprise.”
Statistical analysis software
Insightful Corporation Named in Deloitte's Technology Fast 50 Program for Washington State
Company attributes growing demand for predictive analytics and reporting solutions for its 46 % percent revenue growth over 5 years
SEATTLE - October 1, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today that the company has been named to Deloitte's prestigious Technology Fast 50 Program for Washington State, a ranking of the 50 fastest-growing technology, media, telecommunications, and life sciences companies in the region by Deloitte & Touche USA LLP, one of the nation’s leading professional services organizations. Rankings are based on the percentage revenue growth over five years, from 2002 to 2006.
Insightful’s CEO Jeff Coombs credits the growing demand for predictive analytics and reporting solutions for the company's 46 percent revenue growth from 2002 to 2006. “We are seeing a growing realization by companies across all industries – but particularly life science and financial services – of the imperative to analyze their data and communicate the results efficiently and effectively, unlocking hidden insight and detecting the subtle relationships that can deliver competitive advantage. Our solutions provide enterprises with the knowledge to act quickly and decisively.”
Insightful's increase in revenues of 46 percent from 2002 to 2006 resulted in a ranking of 46th in the Technology Fast 50 for Washington State.
“Deloitte’s Washington State Technology Fast 50 companies have shown the strength, vision and tenacity to succeed in today’s very competitive technology environment,” said Larry Hile, a partner with Deloitte & Touche LLP in Seattle. “We applaud the successes of Insightful and acknowledge it as one of the very few to accomplish such a fast growth rate over the past five years.”
To qualify for the Technology Fast 50, companies must have had operating revenues of at least $50,000 in 2002 and $5,000,000 in 2006, be headquartered in North America, and be a company that owns proprietary technology or proprietary intellectual property that contributes to a significant portion of the company's operating revenues; or devotes a significant proportion of revenues to the research and development of technology. Using other companies' technology or intellectual property in a unique way does not qualify.
Companies from the 16 regional Technology Fast 50 programs in the United States and Canada are automatically entered in Deloitte’s Technology Fast 500 program, which ranks North America’s top 500 fastest growing technology, media, telecommunications and life sciences companies. For more information on Deloitte’s Technology Fast 50 or Technology Fast 500 programs, visit www.fast500.com.
This year’s Washington State Technology Fast 50 program is co-presented by Deloitte & Touche USA LLP and Perkins Coie LLP, Citibank, AH&T Insurance and the Puget Sound Business Journal.
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu, a Swiss Verein, its member firms and their respective subsidiaries and affiliates. As a Swiss Verein (association), neither Deloitte Touche Tohmatsu nor any of its member firms has any liability for each other’s acts or omissions. Each of the member firms is a separate and independent legal entity operating under the names “Deloitte”, “Deloitte & Touche”, “Deloitte Touche Tohmatsu” or other related names. Services are provided by the member firms or their subsidiaries or affiliates and not by the Deloitte Touche Tohmatsu Verein.
Deloitte & Touche USA LLP is the US member firm of Deloitte Touche Tohmatsu. In the U.S., services are provided by the subsidiaries of Deloitte & Touche USA LLP (Deloitte & Touche LLP, Deloitte Consulting LLP, Deloitte Financial Advisory Services LLP, Deloitte Tax LLP and their subsidiaries), and not by Deloitte & Touche USA LLP.
Statistical analysis software
SEATTLE - October 1, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today that the company has been named to Deloitte's prestigious Technology Fast 50 Program for Washington State, a ranking of the 50 fastest-growing technology, media, telecommunications, and life sciences companies in the region by Deloitte & Touche USA LLP, one of the nation’s leading professional services organizations. Rankings are based on the percentage revenue growth over five years, from 2002 to 2006.
Insightful’s CEO Jeff Coombs credits the growing demand for predictive analytics and reporting solutions for the company's 46 percent revenue growth from 2002 to 2006. “We are seeing a growing realization by companies across all industries – but particularly life science and financial services – of the imperative to analyze their data and communicate the results efficiently and effectively, unlocking hidden insight and detecting the subtle relationships that can deliver competitive advantage. Our solutions provide enterprises with the knowledge to act quickly and decisively.”
Insightful's increase in revenues of 46 percent from 2002 to 2006 resulted in a ranking of 46th in the Technology Fast 50 for Washington State.
“Deloitte’s Washington State Technology Fast 50 companies have shown the strength, vision and tenacity to succeed in today’s very competitive technology environment,” said Larry Hile, a partner with Deloitte & Touche LLP in Seattle. “We applaud the successes of Insightful and acknowledge it as one of the very few to accomplish such a fast growth rate over the past five years.”
To qualify for the Technology Fast 50, companies must have had operating revenues of at least $50,000 in 2002 and $5,000,000 in 2006, be headquartered in North America, and be a company that owns proprietary technology or proprietary intellectual property that contributes to a significant portion of the company's operating revenues; or devotes a significant proportion of revenues to the research and development of technology. Using other companies' technology or intellectual property in a unique way does not qualify.
Companies from the 16 regional Technology Fast 50 programs in the United States and Canada are automatically entered in Deloitte’s Technology Fast 500 program, which ranks North America’s top 500 fastest growing technology, media, telecommunications and life sciences companies. For more information on Deloitte’s Technology Fast 50 or Technology Fast 500 programs, visit www.fast500.com.
This year’s Washington State Technology Fast 50 program is co-presented by Deloitte & Touche USA LLP and Perkins Coie LLP, Citibank, AH&T Insurance and the Puget Sound Business Journal.
About Deloitte
Deloitte refers to one or more of Deloitte Touche Tohmatsu, a Swiss Verein, its member firms and their respective subsidiaries and affiliates. As a Swiss Verein (association), neither Deloitte Touche Tohmatsu nor any of its member firms has any liability for each other’s acts or omissions. Each of the member firms is a separate and independent legal entity operating under the names “Deloitte”, “Deloitte & Touche”, “Deloitte Touche Tohmatsu” or other related names. Services are provided by the member firms or their subsidiaries or affiliates and not by the Deloitte Touche Tohmatsu Verein.
Deloitte & Touche USA LLP is the US member firm of Deloitte Touche Tohmatsu. In the U.S., services are provided by the subsidiaries of Deloitte & Touche USA LLP (Deloitte & Touche LLP, Deloitte Consulting LLP, Deloitte Financial Advisory Services LLP, Deloitte Tax LLP and their subsidiaries), and not by Deloitte & Touche USA LLP.
Statistical analysis software
Insightful Corporation Announces 2007 Impact Award Winners
ImClone Systems, Macronix, Time/Warner Retail Sales & Marketing and Zurich Financial Services Honored For Driving Business Benefits and Delivering Competitive Advantage
SEATTLE - October 22, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today that ImClone Systems, Macronix, Time/Warner Retail Sales & Marketing and Zurich Financial Services were awarded with the 2007 Insightful Impact Award at the company’s annual user conference. All four award-winning companies have deployed Insightful’s predictive analytics and reporting solutions, leading to significant impact on their growth and/or profitability.
“This year’s winners have delivered innovative solutions to important issues facing their businesses,” said Jeff Coombs, president and chief executive officer of Insightful. “We are pleased and proud that Insightful’s predictive analytics and reporting solutions have been used to develop substantial cost efficiencies and competitive advantage for our customers.”
Following is a brief description of the award winning applications:
* ImClone Systems, a fully integrated biopharmaceutical company, has deployed S-PLUS® Enterprise Server to create an intuitive system that tracks all of its production metrics, thereby enabling the company to produce products more quickly and efficiently.
* Macronix, a Taiwanese integrated device manufacturer, is providing engineering and business data analysis on demand across its entire production pipeline by implementing Insightful’s S-PLUS Enterprise Server along with customer-developed JavaÒ applications.
* Time/Warner Retail Sales & Marketing, the largest publisher-owned magazine retail marketer and distribution services company in the US, has improved production and distribution profitability as well as created a whole new data business by deploying Insightful’s S-PLUS Enterprise Server as its predictive analytics and reporting solution.
* Zurich Financial Services, an insurance-based financial services provider, utilized S-PLUS Enterprise Server to enable systems from all over the world to share data, models and results within its Risk Modeling Platform.
Statistical analysis software
SEATTLE - October 22, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today that ImClone Systems, Macronix, Time/Warner Retail Sales & Marketing and Zurich Financial Services were awarded with the 2007 Insightful Impact Award at the company’s annual user conference. All four award-winning companies have deployed Insightful’s predictive analytics and reporting solutions, leading to significant impact on their growth and/or profitability.
“This year’s winners have delivered innovative solutions to important issues facing their businesses,” said Jeff Coombs, president and chief executive officer of Insightful. “We are pleased and proud that Insightful’s predictive analytics and reporting solutions have been used to develop substantial cost efficiencies and competitive advantage for our customers.”
Following is a brief description of the award winning applications:
* ImClone Systems, a fully integrated biopharmaceutical company, has deployed S-PLUS® Enterprise Server to create an intuitive system that tracks all of its production metrics, thereby enabling the company to produce products more quickly and efficiently.
* Macronix, a Taiwanese integrated device manufacturer, is providing engineering and business data analysis on demand across its entire production pipeline by implementing Insightful’s S-PLUS Enterprise Server along with customer-developed JavaÒ applications.
* Time/Warner Retail Sales & Marketing, the largest publisher-owned magazine retail marketer and distribution services company in the US, has improved production and distribution profitability as well as created a whole new data business by deploying Insightful’s S-PLUS Enterprise Server as its predictive analytics and reporting solution.
* Zurich Financial Services, an insurance-based financial services provider, utilized S-PLUS Enterprise Server to enable systems from all over the world to share data, models and results within its Risk Modeling Platform.
Statistical analysis software
Insightful Corporation Schedules Announcement of Financial Results for Third Quarter
SEATTLE - October 23, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, has scheduled the announcement of its third quarter 2007 financial results and conference call for Friday, November 2, 2007, after market close. Jeff Coombs, president and CEO of Insightful, and Richard Barber, CFO of Insightful, will be present on the conference call. The call will begin at 2:00 p.m. PT (5:00 p.m. ET).
The company invites the public to submit questions for management via e-mail to investor@insightful.com. Responses to selected questions submitted on or before October 31, 2007, will be provided on the call. Written responses to selected questions about the company’s financial results received after the call, and before the close of business on the first business day after the call, will be posted in the Investor Relations section of the Insightful website (http://www.insightful.com/investors/), and if appropriate, filed with the Securities and Exchange Commission on Form 8-K.
An audio replay of the call will be available beginning at 6:00 p.m. PT the day of the call and ending at Midnight, PT November 9, 2007. An audio archive of the call will be available in MP3 format beginning November 9, 2007 and ending the first business day after the company’s fourth quarter 2007 conference call, in the Investor Relations section of the Insightful website located at http://www.insightful.com/investors/.
Statistical analysis software
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, has scheduled the announcement of its third quarter 2007 financial results and conference call for Friday, November 2, 2007, after market close. Jeff Coombs, president and CEO of Insightful, and Richard Barber, CFO of Insightful, will be present on the conference call. The call will begin at 2:00 p.m. PT (5:00 p.m. ET).
The company invites the public to submit questions for management via e-mail to investor@insightful.com. Responses to selected questions submitted on or before October 31, 2007, will be provided on the call. Written responses to selected questions about the company’s financial results received after the call, and before the close of business on the first business day after the call, will be posted in the Investor Relations section of the Insightful website (http://www.insightful.com/investors/), and if appropriate, filed with the Securities and Exchange Commission on Form 8-K.
An audio replay of the call will be available beginning at 6:00 p.m. PT the day of the call and ending at Midnight, PT November 9, 2007. An audio archive of the call will be available in MP3 format beginning November 9, 2007 and ending the first business day after the company’s fourth quarter 2007 conference call, in the Investor Relations section of the Insightful website located at http://www.insightful.com/investors/.
Statistical analysis software
Insightful Announces Operating Results for Third Quarter 2007
SEATTLE - November 2, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, today announced its operating results for the third quarter ended September 30, 2007.
Insightful reported total revenues of $4.9 million in the third quarter of 2007, a decline of 16% when compared to revenues of $5.9 million in the third quarter of 2006. Total revenues associated with the company’s core data analysis product line decreased by 15% in the third quarter of 2007 compared to the third quarter of 2006. Revenues associated with the company’s text analysis product, InFact, decreased from $0.2 million in the third quarter of 2006 to $0.1 million in the third quarter of 2007. For the third quarter of 2007, Insightful reported net income of $1.9 million, or $0.14 per diluted share, compared to net income of $0.3 million, or $0.02 per diluted share, for the third quarter of 2006. Net income for the third quarter of 2007 includes a gain of $3.5 million on the sale of the company’s InFact search technology and associated intellectual property rights.
Non-GAAP operating results, which excludes stock-based compensation expense, amortization of intangible assets and the gain on the sale of InFact, were a loss of $1.4 million, or $0.11 per diluted share, for the third quarter of 2007, compared to a profit of $0.6 million, or $0.04 per diluted share, for the third quarter of 2006. As described in the section below entitled “Use of Non-GAAP Financial Measures,” non-GAAP earnings or loss differs from net income or loss reported under accounting principles generally accepted in the United States (GAAP) due to the exclusion of stock-based compensation expense, the amortization of intangible assets and the gain on the sale of the company’s InFact search technology and associated intellectual property rights. The reconciliation of Insightful’s GAAP net loss to its non-GAAP loss for the quarters ended September 30, 2007 and 2006 are set forth at the end of this release.
“We are disappointed with our results for the quarter,” said Jeff Coombs, president and CEO of Insightful. “We continue to feel that our strategic direction, to deliver packaged predictive analytic solutions built on an enterprise-scale S-PLUS platform, remains sound. However, we suffered from poor sales execution. Our lower revenues for the third quarter were in part a result of turnover in critical sales functions, of focusing too early on long sales cycles for products and solutions that have not yet been released, and of not closing the larger deals in our pipeline. We also saw declines in our professional services revenues, as custom projects dropped off in anticipation of product releases of packaged solutions.”
Continued Coombs, “We are determined to return to profitability while continuing to build a sound foundation for future revenue growth. To further our strategic enterprise direction, during the third quarter we released S-PLUS 8 Enterprise Server, a new analytic platform that provides improved scalability, reliability and compliance with IT standards for deployment of analytics applications across the enterprise, and continued our development of vertical solutions built on the S-PLUS 8 platform. Additionally, we’ve recognized the need for a unified worldwide sales and operations function. To this end, we are actively recruiting for a vice president of worldwide sales and professional services. Our regional vice presidents have resigned. Our vice president of North American sales has already left the company and our vice president of Europe will be transitioning out by the end of this quarter. The focus of the new function will be to unify and sell one set of worldwide solutions, thereby bringing scalability, simplicity, and effectiveness to our global sales operations.”
Quarterly Highlights
* Software license revenues were $1.6 million in the third quarter of 2007, as compared to $2.3 million in the third quarter of 2006. License revenues from the company’s data analysis product line decreased from $2.1 million to $1.5 million. License revenues from the company’s InFact product decreased from $0.2 million to $0.1 million.
* Maintenance revenues were $2.1 million in the third quarter of 2007, as compared to $1.9 million in the third quarter of 2006.
* Professional services and other revenues were $1.2 million in the third quarter of 2007, as compared to $1.7 million in the third quarter of 2006.
* Funded research, which is an offset to research and development expense in the company’s income statement, was $0.46 million in the third quarter of 2007, as compared to $0.55 million in the third quarter of 2006.
* Domestic revenues were $2.5 million in the third quarter of 2007, as compared to $3.4 million in the third quarter of 2006. This decrease resulted primarily from a $0.7 decrease in revenues from the data analysis product line. International revenues were $2.4 million in the third quarter of 2007, as compared to $2.5 million in the third quarter of 2006.
* The company accelerated its investment in solutions focused on solutions in key vertical markets, and on enhancing its core S-PLUS® predictive analytic platform. As a result, net research and development expenses increased by 17% to $1.4 million in the third quarter of 2007, as compared to $1.2 million in the third quarter of 2006, and increased as a percentage of revenues from 20% to 28%.
* The company released S-PLUS 8 Enterprise Server, the latest release of its predictive analytic platform, and S+FinMetrics® 3.0, the latest release of its software library for advanced financial data analysis.
Use of Non-GAAP Financial Measures
The non-GAAP financial measure of earnings or loss included in this press release is different from the GAAP measure of net income or loss, as this non-GAAP measure excludes certain items otherwise included in the computation of net income or loss. Insightful believes this non-GAAP measure is useful to enhance an overall understanding of its past financial performance and also its prospects for the future. These adjustments to the company’s GAAP results are presented with the intent of providing both management and investors a more complete understanding of Insightful’s underlying operating results and trends. This non-GAAP measure is among the primary indicators management uses as a basis for planning and forecasting of future periods.
The expenses excluded from Insightful’s GAAP results include stock-based compensation expense, amortization of intangible assets arising from the 2004 acquisition from Lucent Technologies, Inc. of the title to the software code underlying the “S” programming language and the gain on the sale of the InFact search technology and associated intellectual property rights. Stock-based compensation expense and amortization of intangible assets have no current effect on cash or the future uses of cash. Insightful’s stock-based compensation expense fluctuates with changes in the company’s stock price and interest rates. For this reason, changes in stock prices and interest rates could mask variation and trends in Insightful’s GAAP operating results that may otherwise be important to an understanding of the company’s results. The acquisition of intangible assets and the sale of the InFact-related technology and intellectual property were events outside of the course of Insightful’s normal business operations. For these reasons, management believes that exclusion of stock-based compensation expense, amortization of intangible assets and the gain in the sale of InFact may be important to an understanding of Insightful’s ongoing operating performance.
Reconciliations of GAAP to non-GAAP results are as follows (in thousands, except share data):
Statistical analysis software
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, today announced its operating results for the third quarter ended September 30, 2007.
Insightful reported total revenues of $4.9 million in the third quarter of 2007, a decline of 16% when compared to revenues of $5.9 million in the third quarter of 2006. Total revenues associated with the company’s core data analysis product line decreased by 15% in the third quarter of 2007 compared to the third quarter of 2006. Revenues associated with the company’s text analysis product, InFact, decreased from $0.2 million in the third quarter of 2006 to $0.1 million in the third quarter of 2007. For the third quarter of 2007, Insightful reported net income of $1.9 million, or $0.14 per diluted share, compared to net income of $0.3 million, or $0.02 per diluted share, for the third quarter of 2006. Net income for the third quarter of 2007 includes a gain of $3.5 million on the sale of the company’s InFact search technology and associated intellectual property rights.
Non-GAAP operating results, which excludes stock-based compensation expense, amortization of intangible assets and the gain on the sale of InFact, were a loss of $1.4 million, or $0.11 per diluted share, for the third quarter of 2007, compared to a profit of $0.6 million, or $0.04 per diluted share, for the third quarter of 2006. As described in the section below entitled “Use of Non-GAAP Financial Measures,” non-GAAP earnings or loss differs from net income or loss reported under accounting principles generally accepted in the United States (GAAP) due to the exclusion of stock-based compensation expense, the amortization of intangible assets and the gain on the sale of the company’s InFact search technology and associated intellectual property rights. The reconciliation of Insightful’s GAAP net loss to its non-GAAP loss for the quarters ended September 30, 2007 and 2006 are set forth at the end of this release.
“We are disappointed with our results for the quarter,” said Jeff Coombs, president and CEO of Insightful. “We continue to feel that our strategic direction, to deliver packaged predictive analytic solutions built on an enterprise-scale S-PLUS platform, remains sound. However, we suffered from poor sales execution. Our lower revenues for the third quarter were in part a result of turnover in critical sales functions, of focusing too early on long sales cycles for products and solutions that have not yet been released, and of not closing the larger deals in our pipeline. We also saw declines in our professional services revenues, as custom projects dropped off in anticipation of product releases of packaged solutions.”
Continued Coombs, “We are determined to return to profitability while continuing to build a sound foundation for future revenue growth. To further our strategic enterprise direction, during the third quarter we released S-PLUS 8 Enterprise Server, a new analytic platform that provides improved scalability, reliability and compliance with IT standards for deployment of analytics applications across the enterprise, and continued our development of vertical solutions built on the S-PLUS 8 platform. Additionally, we’ve recognized the need for a unified worldwide sales and operations function. To this end, we are actively recruiting for a vice president of worldwide sales and professional services. Our regional vice presidents have resigned. Our vice president of North American sales has already left the company and our vice president of Europe will be transitioning out by the end of this quarter. The focus of the new function will be to unify and sell one set of worldwide solutions, thereby bringing scalability, simplicity, and effectiveness to our global sales operations.”
Quarterly Highlights
* Software license revenues were $1.6 million in the third quarter of 2007, as compared to $2.3 million in the third quarter of 2006. License revenues from the company’s data analysis product line decreased from $2.1 million to $1.5 million. License revenues from the company’s InFact product decreased from $0.2 million to $0.1 million.
* Maintenance revenues were $2.1 million in the third quarter of 2007, as compared to $1.9 million in the third quarter of 2006.
* Professional services and other revenues were $1.2 million in the third quarter of 2007, as compared to $1.7 million in the third quarter of 2006.
* Funded research, which is an offset to research and development expense in the company’s income statement, was $0.46 million in the third quarter of 2007, as compared to $0.55 million in the third quarter of 2006.
* Domestic revenues were $2.5 million in the third quarter of 2007, as compared to $3.4 million in the third quarter of 2006. This decrease resulted primarily from a $0.7 decrease in revenues from the data analysis product line. International revenues were $2.4 million in the third quarter of 2007, as compared to $2.5 million in the third quarter of 2006.
* The company accelerated its investment in solutions focused on solutions in key vertical markets, and on enhancing its core S-PLUS® predictive analytic platform. As a result, net research and development expenses increased by 17% to $1.4 million in the third quarter of 2007, as compared to $1.2 million in the third quarter of 2006, and increased as a percentage of revenues from 20% to 28%.
* The company released S-PLUS 8 Enterprise Server, the latest release of its predictive analytic platform, and S+FinMetrics® 3.0, the latest release of its software library for advanced financial data analysis.
Use of Non-GAAP Financial Measures
The non-GAAP financial measure of earnings or loss included in this press release is different from the GAAP measure of net income or loss, as this non-GAAP measure excludes certain items otherwise included in the computation of net income or loss. Insightful believes this non-GAAP measure is useful to enhance an overall understanding of its past financial performance and also its prospects for the future. These adjustments to the company’s GAAP results are presented with the intent of providing both management and investors a more complete understanding of Insightful’s underlying operating results and trends. This non-GAAP measure is among the primary indicators management uses as a basis for planning and forecasting of future periods.
The expenses excluded from Insightful’s GAAP results include stock-based compensation expense, amortization of intangible assets arising from the 2004 acquisition from Lucent Technologies, Inc. of the title to the software code underlying the “S” programming language and the gain on the sale of the InFact search technology and associated intellectual property rights. Stock-based compensation expense and amortization of intangible assets have no current effect on cash or the future uses of cash. Insightful’s stock-based compensation expense fluctuates with changes in the company’s stock price and interest rates. For this reason, changes in stock prices and interest rates could mask variation and trends in Insightful’s GAAP operating results that may otherwise be important to an understanding of the company’s results. The acquisition of intangible assets and the sale of the InFact-related technology and intellectual property were events outside of the course of Insightful’s normal business operations. For these reasons, management believes that exclusion of stock-based compensation expense, amortization of intangible assets and the gain in the sale of InFact may be important to an understanding of Insightful’s ongoing operating performance.
Reconciliations of GAAP to non-GAAP results are as follows (in thousands, except share data):
Statistical analysis software
Insightful Corporation Launches Insightful Clinical Graphics
iCG reduces the time and resources necessary to meet graphics requirements in drug discovery, development and marketing
SEATTLE - December 17, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today the immediate availability of Insightful Clinical Graphics (iCG), a software and services solution for defining and repurposing statistical graphics across all functional areas in drug discovery, development and marketing.
With input from multiple life sciences companies, Insightful designed iCG to simplify the creation and re-use of statistical graphics and elimination of redundant work processes. From discovery and pre-clinical and clinical trials, through marketing and epidemiology, iCG enables scientific data to be presented as actionable information in clinical study reports, regulatory submissions, publications and presentations.
Today, highly skilled statistical programming talent is needed to reprogram, test and QA the numerous statistical graphs used for analysis and reporting of various studies’ data. iCG allows non-programming statisticians, clinicians and medical writers to apply customizable graph templates to study data, and to change a graph style, without compromising the data or the results displayed. iCG is designed to save pharmaceutical companies time and to assist in optimizing the deployment of their expensive statistical programming resources.
"Engaging non-technical workers in the decision-making process and exposing them to the power of statistical analysis represents a significant market opportunity," said Chris Connor, senior research analyst at Health Industry Insights, an IDC company. "In the life science industry, the increasing amount of data being generated is becoming counterproductive. Solutions that can distill data into actionable information and increase focus across the value chain stand to fundamentally change the development process."
“Insightful Clinical Graphics reduces the time and resources needed for graphical review and submission reporting of pre-clinical and clinical data, while providing consistent information flow between functional areas and regulatory agencies,” said Dr. Michael O’Connell, Director of Life Sciences for Insightful. “iCG’s statistical graphics palette and collaborative platform for graphical analysis enable pharmaceutical companies to identify safety and efficacy signals sooner, without burdening their expensive programming assets or risking the integrity of their analyses.”
Insightful Life Sciences Solutions Provide the Knowledge to Act™
Insightful provides life sciences organizations with the knowledge they need to drive faster, better decisions, with specialized analytics, graphics and reporting. Insightful offers software and service solutions in statistical graphics, PK/PD analysis and reporting, clinical data review, clinical trial design, and safety data analysis. These solutions are designed to integrate with existing data management and workflow systems in exploratory and validated environments, to speed the analysis and reporting processes and increase the likelihood of discerning safety and efficacy signals earlier in the drug development process, thus decreasing customers’ product development costs and time to market.
Statistical analysis software
SEATTLE - December 17, 2007 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today the immediate availability of Insightful Clinical Graphics (iCG), a software and services solution for defining and repurposing statistical graphics across all functional areas in drug discovery, development and marketing.
With input from multiple life sciences companies, Insightful designed iCG to simplify the creation and re-use of statistical graphics and elimination of redundant work processes. From discovery and pre-clinical and clinical trials, through marketing and epidemiology, iCG enables scientific data to be presented as actionable information in clinical study reports, regulatory submissions, publications and presentations.
Today, highly skilled statistical programming talent is needed to reprogram, test and QA the numerous statistical graphs used for analysis and reporting of various studies’ data. iCG allows non-programming statisticians, clinicians and medical writers to apply customizable graph templates to study data, and to change a graph style, without compromising the data or the results displayed. iCG is designed to save pharmaceutical companies time and to assist in optimizing the deployment of their expensive statistical programming resources.
"Engaging non-technical workers in the decision-making process and exposing them to the power of statistical analysis represents a significant market opportunity," said Chris Connor, senior research analyst at Health Industry Insights, an IDC company. "In the life science industry, the increasing amount of data being generated is becoming counterproductive. Solutions that can distill data into actionable information and increase focus across the value chain stand to fundamentally change the development process."
“Insightful Clinical Graphics reduces the time and resources needed for graphical review and submission reporting of pre-clinical and clinical data, while providing consistent information flow between functional areas and regulatory agencies,” said Dr. Michael O’Connell, Director of Life Sciences for Insightful. “iCG’s statistical graphics palette and collaborative platform for graphical analysis enable pharmaceutical companies to identify safety and efficacy signals sooner, without burdening their expensive programming assets or risking the integrity of their analyses.”
Insightful Life Sciences Solutions Provide the Knowledge to Act™
Insightful provides life sciences organizations with the knowledge they need to drive faster, better decisions, with specialized analytics, graphics and reporting. Insightful offers software and service solutions in statistical graphics, PK/PD analysis and reporting, clinical data review, clinical trial design, and safety data analysis. These solutions are designed to integrate with existing data management and workflow systems in exploratory and validated environments, to speed the analysis and reporting processes and increase the likelihood of discerning safety and efficacy signals earlier in the drug development process, thus decreasing customers’ product development costs and time to market.
Statistical analysis software
Insightful Solutions for Statistical Modeling/Graphical Analysis of Safety Data in Clinical Trials
Speaker: Dr. Michael O'Connell, Director Life Science Solutions, Insightful Corporation
Download the web cast presentation.
Drug safety issues continue to drive headlines up and stock prices down. The past 12 months have seen failure of high profile drugs in late stage clinical trials (e.g. torcetrapib), withdrawal of drugs from market (e.g. Trayslol) and black-box warnings resulting from academic journal articles (e.g. Avandia).
The Insightful Clinical Graphics™ and Insightful Clinical Review™ Solutions are geared towards rapid and accurate analysis and reporting of safety data. They incorporate targeted statistical and graphical analyses for AE’s, labs and vitals; in alignment with FDA safety guidances e.g. the recent Oct 07 guidance on drug induced liver injury (DILI). The Solutions are designed to address sparse data and multiple endpoints issues with safety data.
Insightful Clinical Graphics provides a standard library of statistical graphics for safety data analysis, and enables simple sharing and re-purposing of graphical analyses in user-friendly interfaces and workflows. Built on the S-PLUS Enterprise Server, Insightful Clinical Graphics is scalable to large groups of statisticians, clinicians and programmers.
Statistically sound safety data analyses, and their resulting interactive graphical/tabular review and report outputs, have immense value for pharmaceutical companies, drug safety monitoring boards and regulatory agencies such as the FDA. We invite statisticians, clinicians and management across the industry to join this webcast, and this movement towards better safety data analysis, clinical review and study reporting.
Michael O'Connell runs the Life Science Solutions effort at Insightful. Michael has been working in the medical device, diagnostics, pharmaceutical and biotech arena for the past 15 years. His background and graduate work was in applied statistics and he has published more than 50 papers on statistical methods and life science applications including calibration, mixed models, and nonparametric regression. He has also written several statistical software packages and libraries. More recently he has been active in the development of tools for analysis and reporting of clinical and safety data from
S-PLUS. Michael holds a Bachelors degree in Science from the University of Sydney, a Masters degree in Statistics from the University of New South Wales and a Ph.D. in Statistics from North Carolina State University.
Statistical analysis software
Download the web cast presentation.
Drug safety issues continue to drive headlines up and stock prices down. The past 12 months have seen failure of high profile drugs in late stage clinical trials (e.g. torcetrapib), withdrawal of drugs from market (e.g. Trayslol) and black-box warnings resulting from academic journal articles (e.g. Avandia).
The Insightful Clinical Graphics™ and Insightful Clinical Review™ Solutions are geared towards rapid and accurate analysis and reporting of safety data. They incorporate targeted statistical and graphical analyses for AE’s, labs and vitals; in alignment with FDA safety guidances e.g. the recent Oct 07 guidance on drug induced liver injury (DILI). The Solutions are designed to address sparse data and multiple endpoints issues with safety data.
Insightful Clinical Graphics provides a standard library of statistical graphics for safety data analysis, and enables simple sharing and re-purposing of graphical analyses in user-friendly interfaces and workflows. Built on the S-PLUS Enterprise Server, Insightful Clinical Graphics is scalable to large groups of statisticians, clinicians and programmers.
Statistically sound safety data analyses, and their resulting interactive graphical/tabular review and report outputs, have immense value for pharmaceutical companies, drug safety monitoring boards and regulatory agencies such as the FDA. We invite statisticians, clinicians and management across the industry to join this webcast, and this movement towards better safety data analysis, clinical review and study reporting.
Michael O'Connell runs the Life Science Solutions effort at Insightful. Michael has been working in the medical device, diagnostics, pharmaceutical and biotech arena for the past 15 years. His background and graduate work was in applied statistics and he has published more than 50 papers on statistical methods and life science applications including calibration, mixed models, and nonparametric regression. He has also written several statistical software packages and libraries. More recently he has been active in the development of tools for analysis and reporting of clinical and safety data from
S-PLUS. Michael holds a Bachelors degree in Science from the University of Sydney, a Masters degree in Statistics from the University of New South Wales and a Ph.D. in Statistics from North Carolina State University.
Statistical analysis software
Risk Management at the Confluence of Strategic & Compliance Imperatives – A Global View
Speaker: Venkat Mullur; Director of Product Marketing and Financial Services, Insightful Corporation
Download the web cast presentation.
Risk management is at the confluence of both regulatory and business imperatives. There exists today a perfect storm of regulatory recommendations that all tend to align minimum required capital levels to true economic risk from exposures. On the other hand, due to increased focus on enterprise risk management, banks and insurance companies are interested in arriving at portfolio loss distributions at a group level. The need for statistical models to quantify the effects of correlations, default risk, loss modeling, and other applications, has never been greater. Aggregating the underlying loss profiles and re-allocating the resulting risk capital back to the real sources of risk is of paramount importance if firms want to take a risk-based view of their asset base.
This presentation will examine the nature of the regulatory requirements, discuss adoption in various geographies, and finally, discuss Insightful Corporation’s enterprise risk aggregation solution and its applications to a broad variety of compliance and business projects.
Mr. Mullur has parleyed a deep knowledge of finance and risk management into a highly successful career in financial services consulting, banking, and product management. As a consultant with IBM (Global Business Services), he worked with some of the world’s largest banks, spanning seven countries. He has been an adviser to banks on issues surrounding Basel II, credit risk management, and the confluence of credit and market risk in the trading book. The combination of quantitative and packaged solutions knowledge he possesses has helped him gain pre-eminence at large financial services clients, and has helped him emerge as one of the top risk management professionals in the consulting world.
Mr. Mullur has contributed to significant intellectual capital growth at IBM, and has delivered internal training sessions, in the US and Hong Kong. He covered topics like Basel II, data challenges in credit risk management, and credit risk modeling techniques. He has lead consulting projects and advisory work top banks in the United States, Canada, Brazil, Hong Kong, and Thailand. He addressed general regulatory and compliance, credit risk management, and Basel II-related challenges. Mr. Mullur has an MBA (finance) from Northwestern’s Kellogg School of Management (MBA). He is currently attending law school, pursuing a JD degree, at Concord University in California.
Statistical analysis software
Download the web cast presentation.
Risk management is at the confluence of both regulatory and business imperatives. There exists today a perfect storm of regulatory recommendations that all tend to align minimum required capital levels to true economic risk from exposures. On the other hand, due to increased focus on enterprise risk management, banks and insurance companies are interested in arriving at portfolio loss distributions at a group level. The need for statistical models to quantify the effects of correlations, default risk, loss modeling, and other applications, has never been greater. Aggregating the underlying loss profiles and re-allocating the resulting risk capital back to the real sources of risk is of paramount importance if firms want to take a risk-based view of their asset base.
This presentation will examine the nature of the regulatory requirements, discuss adoption in various geographies, and finally, discuss Insightful Corporation’s enterprise risk aggregation solution and its applications to a broad variety of compliance and business projects.
Mr. Mullur has parleyed a deep knowledge of finance and risk management into a highly successful career in financial services consulting, banking, and product management. As a consultant with IBM (Global Business Services), he worked with some of the world’s largest banks, spanning seven countries. He has been an adviser to banks on issues surrounding Basel II, credit risk management, and the confluence of credit and market risk in the trading book. The combination of quantitative and packaged solutions knowledge he possesses has helped him gain pre-eminence at large financial services clients, and has helped him emerge as one of the top risk management professionals in the consulting world.
Mr. Mullur has contributed to significant intellectual capital growth at IBM, and has delivered internal training sessions, in the US and Hong Kong. He covered topics like Basel II, data challenges in credit risk management, and credit risk modeling techniques. He has lead consulting projects and advisory work top banks in the United States, Canada, Brazil, Hong Kong, and Thailand. He addressed general regulatory and compliance, credit risk management, and Basel II-related challenges. Mr. Mullur has an MBA (finance) from Northwestern’s Kellogg School of Management (MBA). He is currently attending law school, pursuing a JD degree, at Concord University in California.
Statistical analysis software
Using Differential Evolution for Optimization in Finance
Speaker: David Basterfield, Ph.D., Senior Financial Engineer, Insightful Corporation
Download the webcast presentation.
Differential evolution is a form of genetic or evolutionary algorithm, which is a class of algorithms that uses mechanisms inspired by biological evolution, such as reproduction, mutation and natural selection, to solve difficult optimization problems. Differential evolution was created in 1995 by Kenneth Price and Rainer Storn, and has since earned the reputation of being a very effective global optimizer. The algorithm has a record of reliable and robust performance, particularly in the fields of science and engineering. In this talk, we will look at an implementation of this algorithm in S-PLUS®, and investigate how it may be applied to problems in finance, such as portfolio optimization and model calibration.
Dr David Basterfield joined Insightful Corporation in Seattle as a Senior Financial Engineer, where he is involved with the on-going development of the S+FinMetrics® package in S-PLUS, and for developing financial solutions based on these tools. Dr Basterfield has a Ph.D. in Decision Theory, an MS in Computational Finance, and an MBA. He was the Director of the Computational Finance program at Oregon Health & Sciences University, where he taught from 1999 – 2003, and Associate Professor of Finance at Hillsdale College, from 2004 to 2007. His research interests include derivatives pricing, risk management and optimization methods. Before coming to the USA in 1998, Dr Basterfield worked as a systems architect for CRI, a consultancy company in Luxembourg, whose main client is the European Commission. In his 12 year association with the Commission, Dr Basterfield was involved in many major projects. In particular, he helped design and develop the foreign trade database, their largest information system.
Statistical analysis software
Download the webcast presentation.
Differential evolution is a form of genetic or evolutionary algorithm, which is a class of algorithms that uses mechanisms inspired by biological evolution, such as reproduction, mutation and natural selection, to solve difficult optimization problems. Differential evolution was created in 1995 by Kenneth Price and Rainer Storn, and has since earned the reputation of being a very effective global optimizer. The algorithm has a record of reliable and robust performance, particularly in the fields of science and engineering. In this talk, we will look at an implementation of this algorithm in S-PLUS®, and investigate how it may be applied to problems in finance, such as portfolio optimization and model calibration.
Dr David Basterfield joined Insightful Corporation in Seattle as a Senior Financial Engineer, where he is involved with the on-going development of the S+FinMetrics® package in S-PLUS, and for developing financial solutions based on these tools. Dr Basterfield has a Ph.D. in Decision Theory, an MS in Computational Finance, and an MBA. He was the Director of the Computational Finance program at Oregon Health & Sciences University, where he taught from 1999 – 2003, and Associate Professor of Finance at Hillsdale College, from 2004 to 2007. His research interests include derivatives pricing, risk management and optimization methods. Before coming to the USA in 1998, Dr Basterfield worked as a systems architect for CRI, a consultancy company in Luxembourg, whose main client is the European Commission. In his 12 year association with the Commission, Dr Basterfield was involved in many major projects. In particular, he helped design and develop the foreign trade database, their largest information system.
Statistical analysis software
Applying an Advanced Measurement Approach with Insightful Miner and S-PLUS
Speaker: Guido Maggio, Statistical Consultant, Insightful Corporation
Download the web cast presentation.
Basel II is promoting the implementation of the Advanced Measurement Approach to tackle Operational Risk among banks. An internal model that meets the requirements can generate higher precision and lower Value at Risk. Quantitative information from past losses as well as a qualitative source must be taken into account. The analysis of severities, through Extreme Value Theory is combined to the analysis of frequencies, and a convolution is used to derive the VaR. In this talk, an interesting method to determine the EVT threshold in an automated way will be shown. We will also present an implementation of AMA using Insightful tools: IMINER, S-PLUS, S+FinMetrics and EnvStats; we propose a stable application of EVT and a scenario analysis. In addition, you will also see a solution on how to derive a qualitative VaR from Managers' information that meets Basel II requirements.
Mr Guido Maggio joined Insightful AG in Zurich as a Statistical Consultant. He has an MSc in Social Statistics and a degree in Economic Statistics. His main topics of research have been Variance Estimation, accounting for missing data, and poverty dynamics. He has worked in Italy for MPS Bank in the Operational Risk Department, where he successfully developed an application of the AMA. He has also worked in London for a Mortgage Lender, Platform Home Loans, as well as in Credit Risk and for the University of Birkbeck where he was evaluating governmental projects.
Statistical analysis software
Download the web cast presentation.
Basel II is promoting the implementation of the Advanced Measurement Approach to tackle Operational Risk among banks. An internal model that meets the requirements can generate higher precision and lower Value at Risk. Quantitative information from past losses as well as a qualitative source must be taken into account. The analysis of severities, through Extreme Value Theory is combined to the analysis of frequencies, and a convolution is used to derive the VaR. In this talk, an interesting method to determine the EVT threshold in an automated way will be shown. We will also present an implementation of AMA using Insightful tools: IMINER, S-PLUS, S+FinMetrics and EnvStats; we propose a stable application of EVT and a scenario analysis. In addition, you will also see a solution on how to derive a qualitative VaR from Managers' information that meets Basel II requirements.
Mr Guido Maggio joined Insightful AG in Zurich as a Statistical Consultant. He has an MSc in Social Statistics and a degree in Economic Statistics. His main topics of research have been Variance Estimation, accounting for missing data, and poverty dynamics. He has worked in Italy for MPS Bank in the Operational Risk Department, where he successfully developed an application of the AMA. He has also worked in London for a Mortgage Lender, Platform Home Loans, as well as in Credit Risk and for the University of Birkbeck where he was evaluating governmental projects.
Statistical analysis software
Exposure-Response Based Trial Simulations to Assess Adaptive Designs in Phase II Clinical Trials
Speaker: Simon Zhou, Ph.D., Director of Clinical Pharmacology, Wyeth Research
Download the webcast presentation.
Adaptive design enables clinical trials to adapt to evolving information. It is more powerful and cost effective than traditional design based on formal hypothesis in defining dose response curve and identifying optimal dose in exploratory drug development aiming to learn about pharmacology. When multiple adaptive designs are plausible, clinical trial simulation is a powerful tool to evaluate and differentiate potential outcome of individual adaptive design. It can model complicated dynamic process to evaluate key assumptions in trial design and their impacts on trial outcome. Various types of clinical trial simulations may be conducted to visualize the dynamic trial process from patient recruitment, drug distribution, treatment administration to biomarker, PK/PD and clinical responses.
Integrated with cumulative knowledge of PK/PD and biomarkers, Exposure Response (ER) based trial simulation could assess the validity and robustness of efficacy and safety findings, anticipate problems, project trial outcome. In this presentation, advantages of exposure-response based trial simulations in dosing range phase II studies will be discussed via a case study. Incorporating prior exposure variability and PK/PD information, trial simulations were conducted to (1) evaluate potential adaptive designs via traditional statistical and exposure-response analysis; (2) determine sample size and associated power in demonstrating either utility or futility; (3) define decision criteria based on multiple endpoints; (4) evaluate the robustness of efficacy and safety signals at various stages of study.
Simon Zhou holds Bachelor and Master degrees in Chemistry, a Ph.D. in Pharmaceutics and a Graduate Certificate on Modeling of Complex System from the University of Michigan. Dr. Zhou is currently a director in the department of clinical pharmacology at Wyeth Research in Collegeville, PA. Prior to his current position, he has worked in preclinical and clinical drug development functions addressing biopharmaceutical and trial design issues at Pfizer and Bristol-Myers Squibb. He is experienced in kinetic/dynamic and statistical modeling and simulation to integrate and mine voluminous and complex data from clinical trials. He has published manuscripts in biopharmaceutics, drug delivery and pharmacokinetic and pharmacodynamic modeling.
Statistical analysis software
Download the webcast presentation.
Adaptive design enables clinical trials to adapt to evolving information. It is more powerful and cost effective than traditional design based on formal hypothesis in defining dose response curve and identifying optimal dose in exploratory drug development aiming to learn about pharmacology. When multiple adaptive designs are plausible, clinical trial simulation is a powerful tool to evaluate and differentiate potential outcome of individual adaptive design. It can model complicated dynamic process to evaluate key assumptions in trial design and their impacts on trial outcome. Various types of clinical trial simulations may be conducted to visualize the dynamic trial process from patient recruitment, drug distribution, treatment administration to biomarker, PK/PD and clinical responses.
Integrated with cumulative knowledge of PK/PD and biomarkers, Exposure Response (ER) based trial simulation could assess the validity and robustness of efficacy and safety findings, anticipate problems, project trial outcome. In this presentation, advantages of exposure-response based trial simulations in dosing range phase II studies will be discussed via a case study. Incorporating prior exposure variability and PK/PD information, trial simulations were conducted to (1) evaluate potential adaptive designs via traditional statistical and exposure-response analysis; (2) determine sample size and associated power in demonstrating either utility or futility; (3) define decision criteria based on multiple endpoints; (4) evaluate the robustness of efficacy and safety signals at various stages of study.
Simon Zhou holds Bachelor and Master degrees in Chemistry, a Ph.D. in Pharmaceutics and a Graduate Certificate on Modeling of Complex System from the University of Michigan. Dr. Zhou is currently a director in the department of clinical pharmacology at Wyeth Research in Collegeville, PA. Prior to his current position, he has worked in preclinical and clinical drug development functions addressing biopharmaceutical and trial design issues at Pfizer and Bristol-Myers Squibb. He is experienced in kinetic/dynamic and statistical modeling and simulation to integrate and mine voluminous and complex data from clinical trials. He has published manuscripts in biopharmaceutics, drug delivery and pharmacokinetic and pharmacodynamic modeling.
Statistical analysis software
S+ Seminar: S+ for Financial data Analysis
Event
S-PLUS for Financial Data Analysis
Speaker
David Basterfield, Senior Financial Engineer, Insightful Corporation
Insightful has been serving the world's largest financial services firms such as, Zurich Financial Services, Barclays Global Investors, Bank of America and UBS, for 20 years. Our S-PLUS product family, econometric modules and professional services group provide companies in the financial industry the knowledge to act on their quantitative trading strategies, portfolio optimization methods and risk management requirements.
Agenda
08:30
Registration and Continental Breakfast
09:00
Welcome and Introduction
09:15
Portfolio Optimization with NUOPT
Presented by David Basterfield, Senior Financial Engineer, Insightful Corporation
Insightful will show how S-PLUS with NuOPT™, its optimization package, can be used for quantifying, validating and backtesting portfolio construction and rebalancing strategies that optimize risk-adjusted performance.
10:00
Quantitative Financial Modeling
Presented by David Basterfield, Senior Financial Engineer, Insightful Corporation
Insightful will present an overview of S+FinMetrics 3.0, the latest version of their market-leading time-series analysis and financial econometric modeling tool. This presentation will focus on advanced time-series analysis with GARCH and state-space models, as well as newly introduced derivatives pricing applications.
10:45
Coffee Break
11:00
Value at Risk Modeling and Backtesting with S-PLUS
Presented by David Basterfield, Senior Financial Engineer, Insightful Corporation
Value-at-Risk (VaR) has become a de-facto standard for measuring financial market risk. This presentation will give a brief overview of Value-at-Risk and then walk through the programming details of specifying, estimating, and backtesting different VaR models using S-PLUS and S+FinMetrics.
11:30
Risk Aggregation
11:45
Q & A
12:00
Close
Biography: Dr. David Basterfield
Dr. David Basterfield has recently joined Insightful Corporation in Seattle as a Senior Financial Engineer, where he is involved with the on-going development of the S+Finmetrics package in S-Plus, and for developing financial solutions based on these tools. Dr Basterfield has a Ph. D. in Decision Theory, an MS in Computational Finance and an MBA. He was the Director of the Computational Finance program at Oregon Health & Sciences University, where he taught from 1999 – 2003, and Associate Professor of Finance at Hillsdale College, from 2004 to 2007. His research interests include derivatives pricing, risk management and optimization methods. Before coming to the USA in 1998, Dr Basterfield worked as a systems architect for CRI, a consultancy company in Luxembourg, whose main client is the European Commission. In his 18 year association with the Commission, Dr Basterfield was involved in many major projects. In particular, he helped design and develop the foreign trade database, their largest information system.
Statistical analysis software
S-PLUS for Financial Data Analysis
Speaker
David Basterfield, Senior Financial Engineer, Insightful Corporation
Insightful has been serving the world's largest financial services firms such as, Zurich Financial Services, Barclays Global Investors, Bank of America and UBS, for 20 years. Our S-PLUS product family, econometric modules and professional services group provide companies in the financial industry the knowledge to act on their quantitative trading strategies, portfolio optimization methods and risk management requirements.
Agenda
08:30
Registration and Continental Breakfast
09:00
Welcome and Introduction
09:15
Portfolio Optimization with NUOPT
Presented by David Basterfield, Senior Financial Engineer, Insightful Corporation
Insightful will show how S-PLUS with NuOPT™, its optimization package, can be used for quantifying, validating and backtesting portfolio construction and rebalancing strategies that optimize risk-adjusted performance.
10:00
Quantitative Financial Modeling
Presented by David Basterfield, Senior Financial Engineer, Insightful Corporation
Insightful will present an overview of S+FinMetrics 3.0, the latest version of their market-leading time-series analysis and financial econometric modeling tool. This presentation will focus on advanced time-series analysis with GARCH and state-space models, as well as newly introduced derivatives pricing applications.
10:45
Coffee Break
11:00
Value at Risk Modeling and Backtesting with S-PLUS
Presented by David Basterfield, Senior Financial Engineer, Insightful Corporation
Value-at-Risk (VaR) has become a de-facto standard for measuring financial market risk. This presentation will give a brief overview of Value-at-Risk and then walk through the programming details of specifying, estimating, and backtesting different VaR models using S-PLUS and S+FinMetrics.
11:30
Risk Aggregation
11:45
Q & A
12:00
Close
Biography: Dr. David Basterfield
Dr. David Basterfield has recently joined Insightful Corporation in Seattle as a Senior Financial Engineer, where he is involved with the on-going development of the S+Finmetrics package in S-Plus, and for developing financial solutions based on these tools. Dr Basterfield has a Ph. D. in Decision Theory, an MS in Computational Finance and an MBA. He was the Director of the Computational Finance program at Oregon Health & Sciences University, where he taught from 1999 – 2003, and Associate Professor of Finance at Hillsdale College, from 2004 to 2007. His research interests include derivatives pricing, risk management and optimization methods. Before coming to the USA in 1998, Dr Basterfield worked as a systems architect for CRI, a consultancy company in Luxembourg, whose main client is the European Commission. In his 18 year association with the Commission, Dr Basterfield was involved in many major projects. In particular, he helped design and develop the foreign trade database, their largest information system.
Statistical analysis software
Thursday, September 18, 2008
The S-Plus Package System
Speaker: Dr. Stephen Kaluzny, Insightful Corporation
S-PLUS 8 introduced packages for extending the software. An S-PLUS package can include S-PLUS language code, C/Fortran code, data and documentation. While the package system is used primarily to add new computational techniques to the language, it can also be used to share data, analysis methods and documentation. This presentation gives an overview of the key features of the S-PLUS package system and provides some examples of packages used in pharmaceutical statistics. Creating, testing and distributing a simple package will be demonstrated.
Dr. Stephen Kaluzny is currently a Director of Statistics Research at Insightful Corporation. Stephen previously managed the development team for Insightful's core S-PLUS product. He has over 20 years experience in the development of commercial statistical and data analysis software.
His research interests include statistical visualization methods, computing environments for data analysis and spatial statistics. Stephen holds a B.S. in Ecology from the University of Wisconsin in Green Bay, an M.S. in Statistics from Iowa State University, and a Ph.D. in Biostatistics from the University of Washington.
Statistical analysis software
S-PLUS 8 introduced packages for extending the software. An S-PLUS package can include S-PLUS language code, C/Fortran code, data and documentation. While the package system is used primarily to add new computational techniques to the language, it can also be used to share data, analysis methods and documentation. This presentation gives an overview of the key features of the S-PLUS package system and provides some examples of packages used in pharmaceutical statistics. Creating, testing and distributing a simple package will be demonstrated.
Dr. Stephen Kaluzny is currently a Director of Statistics Research at Insightful Corporation. Stephen previously managed the development team for Insightful's core S-PLUS product. He has over 20 years experience in the development of commercial statistical and data analysis software.
His research interests include statistical visualization methods, computing environments for data analysis and spatial statistics. Stephen holds a B.S. in Ecology from the University of Wisconsin in Green Bay, an M.S. in Statistics from Iowa State University, and a Ph.D. in Biostatistics from the University of Washington.
Statistical analysis software
Insightful Enterprise Risk Aggregation Solution
Speakers: Antoine Beuchat, Senior Consultant, Insightful Switzerland A.G.
Janusz Milek, Director of Financial Analytics, Europe, Insightful Switzerland A.G.
Venkat Mullur, Director of Worldwide Financial Services, Insightful Inc.
Recent economic crises have demonstrated that banks and insurances should improve their risk management practices and estimate economic capital properly to avoid liquidity problems resulting from credit, market, or operational risk. While Basel II and Solvency II guidelines provide a sound basis for economic capital calculations, individual financial institutions still need to implement models, and manage the operational details as part of their compliance activities. Among other things, these details comprise
* Arriving at realistic assumptions to model high-quantile risk dependence structures,
* Designing data flows from individual modeling systems into the economic capital models
* Ensuring data consistency
* Performing what-if analyses, and
* Supporting operational workflows to enable multiple players in the risk management and capital management areas to model and view the results of the risk calculations.
Insightful’s Enterprise Risk Aggregation (ERA) solution squarely addresses the above issues. It constitutes a flexible and cost-effective solution, capable of economic capital calculations in both the banking and insurance areas. The foundation on which the Insightful ERA solution rests is S+, our robust and time-tested statistical modeling platform. With S+, complex simulation models can be created, leveraged, and efficiently run within minutes.
In addition, the ERA solution consists of a cutting-edge metadata management component, RiskHyperCUBE, and SolvSim, which is a simulation engine that can take in a variety of input types for marginal distributions, uses copulas to model the aggregate distribution and complex dependence structures.
During our web presentation, we will demonstrate parts of the solution through a graphical user interface, to walk the participant through a typical risk aggregation workflow, from input specification, simulation, to relevant risk-related graphical outputs.
Antoine Beuchat is Senior Consultant at Insightful in Financial Services Switzerland holding a Masters degree in micro-engineering from the Swiss Federal Institute of Technology (EPFL). Mr. Beuchat has been involved as pre-sales consultant and leader in the development of customer and proprietary solution in the risk modeling and management area for major international insurances. During his working experiences and his education Mr. Beuchat had the opportunity to apply variety of sophisticated numerical techniques to analyze statistical models in the financial industry. His topics of interest include quantitative finance in risk and asset management.
Dr. Janusz Milek works at Insightful Corporation as a Director of Financial Analytics, Europe. He is involved in the solution development, pre-sales activities, and customer projects in the area of enterprise-wide risk management, supply chain optimization, tax yield prediction, and data quality monitoring. Mr. Milek holds a Master's degree in Electronics Engineering from Warsaw Technical University and a Doctorate in Technical Sciences from the Swiss Federal Institute of Technology (ETH) in Zurich. His experience, training, and research comprise risk modeling, time series analysis, process control, and signal processing. Dr. Milek delivers courses on financial and industrial applications of time series analysis.
Venkat Mullur is Director, Worldwide Financial Services at Insightful Corporation. His topics of interest include financial risk management, global regulatory & compliance imperatives, and application of portfolio methods to capital management. Mr. Mullur divides his time between North America and Europe, working with key Insightful customers, supporting pre-sales activities, and presenting at leading finance forums. Prior to Insightful, Mr. Mullur was at IBM (Global Business Services) where he advised large global banks on Basel II-related compliance activities. Mr. Mullur holds an MBA in finance from Northwestern University’s Kellogg School of Management, and has a Master’s degree in mechanical engineering from the University of Miami. At industry forums, he has presented on a variety of contemporary topics in the world of finance. He is a regulator contributor to Wilmott Magazine, a leading publication serving the quantitative finance community.
Statistical analysis software
Janusz Milek, Director of Financial Analytics, Europe, Insightful Switzerland A.G.
Venkat Mullur, Director of Worldwide Financial Services, Insightful Inc.
Recent economic crises have demonstrated that banks and insurances should improve their risk management practices and estimate economic capital properly to avoid liquidity problems resulting from credit, market, or operational risk. While Basel II and Solvency II guidelines provide a sound basis for economic capital calculations, individual financial institutions still need to implement models, and manage the operational details as part of their compliance activities. Among other things, these details comprise
* Arriving at realistic assumptions to model high-quantile risk dependence structures,
* Designing data flows from individual modeling systems into the economic capital models
* Ensuring data consistency
* Performing what-if analyses, and
* Supporting operational workflows to enable multiple players in the risk management and capital management areas to model and view the results of the risk calculations.
Insightful’s Enterprise Risk Aggregation (ERA) solution squarely addresses the above issues. It constitutes a flexible and cost-effective solution, capable of economic capital calculations in both the banking and insurance areas. The foundation on which the Insightful ERA solution rests is S+, our robust and time-tested statistical modeling platform. With S+, complex simulation models can be created, leveraged, and efficiently run within minutes.
In addition, the ERA solution consists of a cutting-edge metadata management component, RiskHyperCUBE, and SolvSim, which is a simulation engine that can take in a variety of input types for marginal distributions, uses copulas to model the aggregate distribution and complex dependence structures.
During our web presentation, we will demonstrate parts of the solution through a graphical user interface, to walk the participant through a typical risk aggregation workflow, from input specification, simulation, to relevant risk-related graphical outputs.
Antoine Beuchat is Senior Consultant at Insightful in Financial Services Switzerland holding a Masters degree in micro-engineering from the Swiss Federal Institute of Technology (EPFL). Mr. Beuchat has been involved as pre-sales consultant and leader in the development of customer and proprietary solution in the risk modeling and management area for major international insurances. During his working experiences and his education Mr. Beuchat had the opportunity to apply variety of sophisticated numerical techniques to analyze statistical models in the financial industry. His topics of interest include quantitative finance in risk and asset management.
Dr. Janusz Milek works at Insightful Corporation as a Director of Financial Analytics, Europe. He is involved in the solution development, pre-sales activities, and customer projects in the area of enterprise-wide risk management, supply chain optimization, tax yield prediction, and data quality monitoring. Mr. Milek holds a Master's degree in Electronics Engineering from Warsaw Technical University and a Doctorate in Technical Sciences from the Swiss Federal Institute of Technology (ETH) in Zurich. His experience, training, and research comprise risk modeling, time series analysis, process control, and signal processing. Dr. Milek delivers courses on financial and industrial applications of time series analysis.
Venkat Mullur is Director, Worldwide Financial Services at Insightful Corporation. His topics of interest include financial risk management, global regulatory & compliance imperatives, and application of portfolio methods to capital management. Mr. Mullur divides his time between North America and Europe, working with key Insightful customers, supporting pre-sales activities, and presenting at leading finance forums. Prior to Insightful, Mr. Mullur was at IBM (Global Business Services) where he advised large global banks on Basel II-related compliance activities. Mr. Mullur holds an MBA in finance from Northwestern University’s Kellogg School of Management, and has a Master’s degree in mechanical engineering from the University of Miami. At industry forums, he has presented on a variety of contemporary topics in the world of finance. He is a regulator contributor to Wilmott Magazine, a leading publication serving the quantitative finance community.
Statistical analysis software
End To-End Statistical Reporting With S-Plus, SAS and Insightful Solutions
Speaker: Michael O'Connell, Insightful Corporation
Statisticians and programmers have significant responsibilities in the management and analysis of data. On one hand, they need a statistical and graphical analysis sand-box in order to explore and understand study data. On the other hand, they need robust, repeatable methods for production output in a regulated data management and analysis environment. For example, in the pharmaceutical world, these tasks are sometimes referred to as clinical data review and clinical study reporting, respectively.
SAS® software and S-PLUS are widely used together in data analysis environments. A typical use case involves the SAS data step for data transformation and PROC Tabulate to provide simple tabular summaries; and S-PLUS to provide graphical summaries and additional analytics, e.g. survival analysis, from the transformed SAS datasets.
This web cast describes the use of S-PLUS and Insightful Solutions for end-to-end graphical and tabular reporting including:
* The S-PLUS Connector for SAS – batch analyses using S-PLUS and SAS software, including combining S-PLUS graphics with SAS tabular output e.g. as SAS ODS html, and automated integration of S-PLUS and SAS verbose log files.
* Insightful Clinical Study Reporting – static, pixel-perfect reporting for study reports and regulatory submissions.
* Insightful Clinical Review – interactive graphical and tabular reports for exploratory analysis, data cleaning and review of clinical trials.
* Insightful Clinical Graphics – point-click graphics with generated S-PLUS code for clinical data analysis and reporting
S-PLUS and SAS software together provide a highly efficient environment for exploratory and production analysis and reporting, leveraging the strengths of each tool. The Insightful Solutions provide automated processes and software for standardized study reports that save time, standardize best practices, remove manual errors and allow high-value professionals to spend their time advancing their research programs rather than spending time churning through manual graphics and reporting tasks.
Statistical analysis software
Statisticians and programmers have significant responsibilities in the management and analysis of data. On one hand, they need a statistical and graphical analysis sand-box in order to explore and understand study data. On the other hand, they need robust, repeatable methods for production output in a regulated data management and analysis environment. For example, in the pharmaceutical world, these tasks are sometimes referred to as clinical data review and clinical study reporting, respectively.
SAS® software and S-PLUS are widely used together in data analysis environments. A typical use case involves the SAS data step for data transformation and PROC Tabulate to provide simple tabular summaries; and S-PLUS to provide graphical summaries and additional analytics, e.g. survival analysis, from the transformed SAS datasets.
This web cast describes the use of S-PLUS and Insightful Solutions for end-to-end graphical and tabular reporting including:
* The S-PLUS Connector for SAS – batch analyses using S-PLUS and SAS software, including combining S-PLUS graphics with SAS tabular output e.g. as SAS ODS html, and automated integration of S-PLUS and SAS verbose log files.
* Insightful Clinical Study Reporting – static, pixel-perfect reporting for study reports and regulatory submissions.
* Insightful Clinical Review – interactive graphical and tabular reports for exploratory analysis, data cleaning and review of clinical trials.
* Insightful Clinical Graphics – point-click graphics with generated S-PLUS code for clinical data analysis and reporting
S-PLUS and SAS software together provide a highly efficient environment for exploratory and production analysis and reporting, leveraging the strengths of each tool. The Insightful Solutions provide automated processes and software for standardized study reports that save time, standardize best practices, remove manual errors and allow high-value professionals to spend their time advancing their research programs rather than spending time churning through manual graphics and reporting tasks.
Statistical analysis software
Graphical Evaluation for Cardiovascular Safety Data in Clinical Trials
Speaker: Ihab G. Girgis; Johnson & Johnson Pharmaceutical R&D
Detecting drug-induced effect on cardiac repolarization or QT interval is a closely monitored safety element in drug development and more recently, it is thoroughly scrutinized in regulatory submissions. Length of the QT intervals can be influenced by a number of covariates, such as, heart rate (HR), RR interval (RR= 60/HR), gender, and natural circadian rhythm.
There are other unknown factors that influence this interval, making it highly variable across population and the analysis of such data is complex. In order to evaluate the effect of drug on QT interval, accurate modeling of drug-free baseline QT becomes an important first step; the changes to this baseline model after the administration of investigational drug will reflect the effect of the investigational drug on the QT/QTc interval.
This work focuses on the graphical evaluation of baseline QT clinical data and its modeling. A hierarchical Bayesian approach has been used. The QT-RR relationship is explored using various models and performance of the models is evaluated in comparison to well-known correlation methods (Bazett, Fridericia, Framingham, Hodge and individual correction). Finally, diverse nonlinear functions, ranging from a simple cosine function to multi-harmonics Fourier series are tested to describe the circadian rhythm effect.
Statistical analysis software
Detecting drug-induced effect on cardiac repolarization or QT interval is a closely monitored safety element in drug development and more recently, it is thoroughly scrutinized in regulatory submissions. Length of the QT intervals can be influenced by a number of covariates, such as, heart rate (HR), RR interval (RR= 60/HR), gender, and natural circadian rhythm.
There are other unknown factors that influence this interval, making it highly variable across population and the analysis of such data is complex. In order to evaluate the effect of drug on QT interval, accurate modeling of drug-free baseline QT becomes an important first step; the changes to this baseline model after the administration of investigational drug will reflect the effect of the investigational drug on the QT/QTc interval.
This work focuses on the graphical evaluation of baseline QT clinical data and its modeling. A hierarchical Bayesian approach has been used. The QT-RR relationship is explored using various models and performance of the models is evaluated in comparison to well-known correlation methods (Bazett, Fridericia, Framingham, Hodge and individual correction). Finally, diverse nonlinear functions, ranging from a simple cosine function to multi-harmonics Fourier series are tested to describe the circadian rhythm effect.
Statistical analysis software
End-To-End Statistical Reporting With S-Plus, SAS and Insightful Solutions
Date: Thursday, September 25, 2008
Speaker: Michael O'Connell, Insightful Corporation
Times: 2:00pm BST; 3:00pm CEST; 9:00am EDT; 6:00am PDT
*Register for this webcast
*Download troubleshooting instructions for logging in to the webcast
Statisticians and programmers have significant responsibilities in the management and analysis of data. On one hand, they need a statistical and graphical analysis sand-box in order to explore and understand study data. On the other hand, they need robust, repeatable methods for production output in a regulated data management and analysis environment. For example, in the pharmaceutical world, these tasks are sometimes referred to as clinical data review and clinical study reporting, respectively.
SAS® software and S-PLUS are widely used together in data analysis environments. A typical use case involves the SAS data step for data transformation and PROC Tabulate to provide simple tabular summaries; and S-PLUS to provide graphical summaries and additional analytics, e.g. survival analysis, from the transformed SAS datasets.
This web cast describes the use of S-PLUS and Insightful Solutions for end-to-end graphical and tabular reporting including:
* The S-PLUS Connector for SAS – batch analyses using S-PLUS and SAS software, including combining S-PLUS graphics with SAS tabular output e.g. as SAS ODS html, and automated integration of S-PLUS and SAS verbose log files.
* Insightful Clinical Study Reporting – static, pixel-perfect reporting for study reports and regulatory submissions.
* Insightful Clinical Review – interactive graphical and tabular reports for exploratory analysis, data cleaning and review of clinical trials.
* Insightful Clinical Graphics – point-click graphics with generated S-PLUS code for clinical data analysis and reporting
S-PLUS and SAS software together provide a highly efficient environment for exploratory and production analysis and reporting, leveraging the strengths of each tool. The Insightful Solutions provide automated processes and software for standardized study reports that save time, standardize best practices, remove manual errors and allow high-value professionals to spend their time advancing their research programs rather than spending time churning through manual graphics and reporting tasks.
Statistical analysis software
Speaker: Michael O'Connell, Insightful Corporation
Times: 2:00pm BST; 3:00pm CEST; 9:00am EDT; 6:00am PDT
*Register for this webcast
*Download troubleshooting instructions for logging in to the webcast
Statisticians and programmers have significant responsibilities in the management and analysis of data. On one hand, they need a statistical and graphical analysis sand-box in order to explore and understand study data. On the other hand, they need robust, repeatable methods for production output in a regulated data management and analysis environment. For example, in the pharmaceutical world, these tasks are sometimes referred to as clinical data review and clinical study reporting, respectively.
SAS® software and S-PLUS are widely used together in data analysis environments. A typical use case involves the SAS data step for data transformation and PROC Tabulate to provide simple tabular summaries; and S-PLUS to provide graphical summaries and additional analytics, e.g. survival analysis, from the transformed SAS datasets.
This web cast describes the use of S-PLUS and Insightful Solutions for end-to-end graphical and tabular reporting including:
* The S-PLUS Connector for SAS – batch analyses using S-PLUS and SAS software, including combining S-PLUS graphics with SAS tabular output e.g. as SAS ODS html, and automated integration of S-PLUS and SAS verbose log files.
* Insightful Clinical Study Reporting – static, pixel-perfect reporting for study reports and regulatory submissions.
* Insightful Clinical Review – interactive graphical and tabular reports for exploratory analysis, data cleaning and review of clinical trials.
* Insightful Clinical Graphics – point-click graphics with generated S-PLUS code for clinical data analysis and reporting
S-PLUS and SAS software together provide a highly efficient environment for exploratory and production analysis and reporting, leveraging the strengths of each tool. The Insightful Solutions provide automated processes and software for standardized study reports that save time, standardize best practices, remove manual errors and allow high-value professionals to spend their time advancing their research programs rather than spending time churning through manual graphics and reporting tasks.
Statistical analysis software
Insightful Webinar on “Graphical Evaluation for Cardiovascular Safety Data in Clinical Trials”
Insightful Corporation Announces Webinar on “Graphical Evaluation for Cardiovascular Safety Data in Clinical Trials”
Principal Scientist in the Advanced Modeling and Simulation group at Johnson & Johnson Pharmaceutical Research & Development to Present
Seattle - July 16, 2008 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today the company will host a free webinar titled “Graphical Evaluation for Cardiovascular Safety Data in Clinical Trials” on July 17, 2008 from 8:30 to 9:30 am, Pacific Daylight Time. Dr. Ihab G. Girgis, a principal scientist in the Advanced Modeling and Simulation group at Johnson & Johnson Pharmaceutical Research & Development, will lead the webinar.
During the webinar, Dr. Girgis will explore the graphical evaluation of baseline QT clinical data and its modeling. Detecting the drug-induced effect on cardiac repolarization or QT interval is a closely monitored safety element in drug development and more recently, it has also been scrutinized in regulatory submissions. Length of the QT intervals can be influenced by a number of covariates, such as heart rate (HR), RR interval (RR= 60/HR), gender, and natural circadian rhythm.
There are other unknown factors that influence this interval, making it highly variable across populations. As such, the analysis of these data is complex. In order to evaluate the effect of drug on QT interval, accurate modeling of drug-free baseline QT becomes an important first step. Subsequent changes to this baseline model after the administration of an investigational drug can reflect the effect of the investigational drug on the QT/QTc interval.
Statistical analysis software
Principal Scientist in the Advanced Modeling and Simulation group at Johnson & Johnson Pharmaceutical Research & Development to Present
Seattle - July 16, 2008 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, announced today the company will host a free webinar titled “Graphical Evaluation for Cardiovascular Safety Data in Clinical Trials” on July 17, 2008 from 8:30 to 9:30 am, Pacific Daylight Time. Dr. Ihab G. Girgis, a principal scientist in the Advanced Modeling and Simulation group at Johnson & Johnson Pharmaceutical Research & Development, will lead the webinar.
During the webinar, Dr. Girgis will explore the graphical evaluation of baseline QT clinical data and its modeling. Detecting the drug-induced effect on cardiac repolarization or QT interval is a closely monitored safety element in drug development and more recently, it has also been scrutinized in regulatory submissions. Length of the QT intervals can be influenced by a number of covariates, such as heart rate (HR), RR interval (RR= 60/HR), gender, and natural circadian rhythm.
There are other unknown factors that influence this interval, making it highly variable across populations. As such, the analysis of these data is complex. In order to evaluate the effect of drug on QT interval, accurate modeling of drug-free baseline QT becomes an important first step. Subsequent changes to this baseline model after the administration of an investigational drug can reflect the effect of the investigational drug on the QT/QTc interval.
Statistical analysis software
Insightful Corporation Schedules Announcement of Financial Results for Second Quarter 2008
Seattle - July 17, 2008 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, has scheduled the announcement of its second quarter 2008 financial results for Thursday, July 31, 2008, after market close.
The company invites the public to submit questions for management about the company’s financial results via e-mail to investor@insightful.com. Written responses to selected questions received before the close of business on the first business day after the release will be posted in the Investor Relations section of the Insightful website (http://www.insightful.com/investors/), and if appropriate, filed with the Securities and Exchange Commission on Form 8-K.
Should you have any questions prior to the call, please call Insightful’s investor relations desk at (206) 283-8802 or e-mail investor@insightful.com.
Statistical analysis software
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, has scheduled the announcement of its second quarter 2008 financial results for Thursday, July 31, 2008, after market close.
The company invites the public to submit questions for management about the company’s financial results via e-mail to investor@insightful.com. Written responses to selected questions received before the close of business on the first business day after the release will be posted in the Investor Relations section of the Insightful website (http://www.insightful.com/investors/), and if appropriate, filed with the Securities and Exchange Commission on Form 8-K.
Should you have any questions prior to the call, please call Insightful’s investor relations desk at (206) 283-8802 or e-mail investor@insightful.com.
Statistical analysis software
Insightful Corporation Announces Operating Results for Second Quarter 2008
Insightful Also Provides Update Regarding Proposed Acquisition by TIBCO Software Inc.
SEATTLE - July 31, 2008 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, today announced its operating results for the second quarter ended June 30, 2008.
Insightful reported total revenues of $5.4 million for the second quarter of 2008, a decrease of 2% compared to revenues of $5.5 million for the second quarter of 2007. For the second quarter of 2008, Insightful reported a net loss of $0.5 million, or $0.04 per share, compared to a net loss of $1.3 million, or $0.10 per share, for the second quarter of 2007. Included in operating results for the second quarter of 2008 are $0.8 million in expenses directly related with the proposed acquisition of the company by TIBCO Software Inc.
Non-GAAP operating results, which exclude stock-based compensation expense, were a loss of $0.3 million, or $0.03 per share, for the second quarter of 2008, compared to a loss of $1.1 million, or $0.08 per share, for the second quarter of 2007. As described in the section below entitled “Use of Non-GAAP Financial Measures,” non-GAAP earnings or loss differs from net income or loss reported under accounting principles generally accepted in the United States (GAAP) due to the exclusion of stock-based compensation expense. The reconciliation of Insightful’s GAAP net loss to its non-GAAP operating results for the quarters ended June 30, 2008 and 2007 are set forth at the end of this release.
“We are pleased that, excluding costs directly associated with the proposed acquisition by TIBCO, we were profitable for the quarter,” said Jeff Coombs, president and CEO of Insightful.
Insightful’s cash, cash equivalents, and short and long-term investments totaled $11.5 million at June 30, 2008, up from $11.0 million at December 31, 2007.
On June 19, 2008, Insightful announced that it had signed a definitive agreement to be acquired by TIBCO Software Inc. (NASDAQ: TIBX) in a transaction valued at approximately $25 million, including the value of certain assumed options. Under the terms of the agreement, following the closing, Insightful stockholders will receive $1.87 in cash for each outstanding share of common stock they own. On July 24, 2008, Insightful filed a definitive proxy statement related to the proposed acquisition with the Securities and Exchange Commission (SEC). Insightful will hold a special meeting of stockholders to vote on the proposed transaction at 9:00 a.m. Seattle time on August 29, 2008, at the offices of RR Donnelly, 999 Third Avenue, Suite 3201, Seattle, Washington 98104. Stockholders at the close of business on July 8, 2008, the record date, will be entitled to vote on the transaction.
Quarterly Financial Highlights
* Software license revenues were $1.7 million in the second quarters of both 2008 and 2007. License revenues declined slightly domestically and increased slightly internationally.
* Maintenance revenues were $2.3 million in the second quarter of 2008, compared to $2.0 million in the second quarter of 2007. Maintenance revenue increased primarily as a result of increases in our maintenance renewal rates and the late renewal of a number of maintenance contracts that had expired in earlier quarters.
* Professional services and other revenues were $1.4 million in the second quarter of 2008, compared to $1.8 million in the second quarter of 2007. The entire decline in professional services revenues was in the company’s domestic segment.
* Domestic revenues were $2.5 million in the second quarter of 2008, compared to $2.9 million in the second quarter of 2007. International revenues were $2.9 million in the second quarter of 2008, compared to $2.6 million in the second quarter of 2007.
* Funded research, which is an offset to research and development expense in the company’s income statement, was $250,000 in the second quarter of 2008, compared to $511,000 in the second quarter of 2007. The decline was due to several billable research employees moving to our data analysis product group in 2007 and several others leaving Insightful in 2007 and 2008.
* Total operating expenses were $4.8 million in the second quarter of 2008, including $0.8 million in expenses directly related to the proposed acquisition of the company by TIBCO Software Inc., compared to $5.4 million in the second quarter of 2007.
* Gross profit margin was 77% in the second quarter of 2008 compared to 73% in the second quarter of 2007. The increase resulted from a smaller percentage of the company’s total revenues coming from lower-margin professional services revenues in 2008 than in 2007.
Use of Non-GAAP Financial Measures
The non-GAAP financial measure of loss included in this press release is different from the GAAP measure of net loss, as this non-GAAP measure excludes certain expenses otherwise included in the computation of net loss. Insightful believes this non-GAAP measure is useful to enhance an overall understanding of its past financial performance and also its prospects for the future. These adjustments to the company’s GAAP results are presented with the intent of providing both management and investors a more complete understanding of Insightful’s underlying operating results and trends. This non-GAAP measure is among the primary indicators management uses as a basis for planning and forecasting of future periods.
The expenses excluded from Insightful’s GAAP results for the second quarters of 2008 and 2007 include stock-based compensation expense. Stock-based compensation expense has no current effect on cash or the future uses of cash. Insightful’s stock-based compensation expense fluctuates with changes in the company’s stock price and interest rates. For this reason, changes in stock prices and interest rates could mask variation and trends in Insightful’s GAAP operating results that may otherwise be important to an understanding of the company’s results. For these reasons, management believes that exclusion of stock-based compensation expense may be important to an understanding of Insightful’s ongoing operating performance.
Reconciliations of GAAP to non-GAAP results are as follows (in thousands, except share data):
Statistical analysis software
SEATTLE - July 31, 2008 -
Insightful Corporation (NASDAQ: IFUL), a leading provider of predictive analytics and reporting solutions, today announced its operating results for the second quarter ended June 30, 2008.
Insightful reported total revenues of $5.4 million for the second quarter of 2008, a decrease of 2% compared to revenues of $5.5 million for the second quarter of 2007. For the second quarter of 2008, Insightful reported a net loss of $0.5 million, or $0.04 per share, compared to a net loss of $1.3 million, or $0.10 per share, for the second quarter of 2007. Included in operating results for the second quarter of 2008 are $0.8 million in expenses directly related with the proposed acquisition of the company by TIBCO Software Inc.
Non-GAAP operating results, which exclude stock-based compensation expense, were a loss of $0.3 million, or $0.03 per share, for the second quarter of 2008, compared to a loss of $1.1 million, or $0.08 per share, for the second quarter of 2007. As described in the section below entitled “Use of Non-GAAP Financial Measures,” non-GAAP earnings or loss differs from net income or loss reported under accounting principles generally accepted in the United States (GAAP) due to the exclusion of stock-based compensation expense. The reconciliation of Insightful’s GAAP net loss to its non-GAAP operating results for the quarters ended June 30, 2008 and 2007 are set forth at the end of this release.
“We are pleased that, excluding costs directly associated with the proposed acquisition by TIBCO, we were profitable for the quarter,” said Jeff Coombs, president and CEO of Insightful.
Insightful’s cash, cash equivalents, and short and long-term investments totaled $11.5 million at June 30, 2008, up from $11.0 million at December 31, 2007.
On June 19, 2008, Insightful announced that it had signed a definitive agreement to be acquired by TIBCO Software Inc. (NASDAQ: TIBX) in a transaction valued at approximately $25 million, including the value of certain assumed options. Under the terms of the agreement, following the closing, Insightful stockholders will receive $1.87 in cash for each outstanding share of common stock they own. On July 24, 2008, Insightful filed a definitive proxy statement related to the proposed acquisition with the Securities and Exchange Commission (SEC). Insightful will hold a special meeting of stockholders to vote on the proposed transaction at 9:00 a.m. Seattle time on August 29, 2008, at the offices of RR Donnelly, 999 Third Avenue, Suite 3201, Seattle, Washington 98104. Stockholders at the close of business on July 8, 2008, the record date, will be entitled to vote on the transaction.
Quarterly Financial Highlights
* Software license revenues were $1.7 million in the second quarters of both 2008 and 2007. License revenues declined slightly domestically and increased slightly internationally.
* Maintenance revenues were $2.3 million in the second quarter of 2008, compared to $2.0 million in the second quarter of 2007. Maintenance revenue increased primarily as a result of increases in our maintenance renewal rates and the late renewal of a number of maintenance contracts that had expired in earlier quarters.
* Professional services and other revenues were $1.4 million in the second quarter of 2008, compared to $1.8 million in the second quarter of 2007. The entire decline in professional services revenues was in the company’s domestic segment.
* Domestic revenues were $2.5 million in the second quarter of 2008, compared to $2.9 million in the second quarter of 2007. International revenues were $2.9 million in the second quarter of 2008, compared to $2.6 million in the second quarter of 2007.
* Funded research, which is an offset to research and development expense in the company’s income statement, was $250,000 in the second quarter of 2008, compared to $511,000 in the second quarter of 2007. The decline was due to several billable research employees moving to our data analysis product group in 2007 and several others leaving Insightful in 2007 and 2008.
* Total operating expenses were $4.8 million in the second quarter of 2008, including $0.8 million in expenses directly related to the proposed acquisition of the company by TIBCO Software Inc., compared to $5.4 million in the second quarter of 2007.
* Gross profit margin was 77% in the second quarter of 2008 compared to 73% in the second quarter of 2007. The increase resulted from a smaller percentage of the company’s total revenues coming from lower-margin professional services revenues in 2008 than in 2007.
Use of Non-GAAP Financial Measures
The non-GAAP financial measure of loss included in this press release is different from the GAAP measure of net loss, as this non-GAAP measure excludes certain expenses otherwise included in the computation of net loss. Insightful believes this non-GAAP measure is useful to enhance an overall understanding of its past financial performance and also its prospects for the future. These adjustments to the company’s GAAP results are presented with the intent of providing both management and investors a more complete understanding of Insightful’s underlying operating results and trends. This non-GAAP measure is among the primary indicators management uses as a basis for planning and forecasting of future periods.
The expenses excluded from Insightful’s GAAP results for the second quarters of 2008 and 2007 include stock-based compensation expense. Stock-based compensation expense has no current effect on cash or the future uses of cash. Insightful’s stock-based compensation expense fluctuates with changes in the company’s stock price and interest rates. For this reason, changes in stock prices and interest rates could mask variation and trends in Insightful’s GAAP operating results that may otherwise be important to an understanding of the company’s results. For these reasons, management believes that exclusion of stock-based compensation expense may be important to an understanding of Insightful’s ongoing operating performance.
Reconciliations of GAAP to non-GAAP results are as follows (in thousands, except share data):
Statistical analysis software
Exploratory Analysis of Gene Expression Data
RES –
Microarray’s are an exciting new technology allowing the simultaneous collection of gene expression data for literally thousands of genes. Recent developments have made this technology accessible to an ever widening group of researchers, and in the near future microarray technology may become reliable enough and inexpensive enough for routine medical use. Data arising from microarray’s can be used to differentiate cell types (e.g., cancerous versus noncancerous), and for many other purposes. Researchers are only beginning to explore the potential uses of this new technology.
The main difficulty in the analysis of microarray data is its relative abundance: gene expression information for thousands of genes is gathered for a relatively small number of experiments. Simple hypothesis tests must account for multiple testing, and standard statistical methods like multidimensional scaling and cluster analysis must be used on very large matrices, matrices that may be too big even fit into a computers memory.
Use of microarray methodologies will grow exponentially over the next few years as signal extraction and other aspects of the technology mature. Statistical analysis methods that extend a readily available statistical analysis package such as S-PLUS and are also capable of handling
Statistical analysis software
Microarray’s are an exciting new technology allowing the simultaneous collection of gene expression data for literally thousands of genes. Recent developments have made this technology accessible to an ever widening group of researchers, and in the near future microarray technology may become reliable enough and inexpensive enough for routine medical use. Data arising from microarray’s can be used to differentiate cell types (e.g., cancerous versus noncancerous), and for many other purposes. Researchers are only beginning to explore the potential uses of this new technology.
The main difficulty in the analysis of microarray data is its relative abundance: gene expression information for thousands of genes is gathered for a relatively small number of experiments. Simple hypothesis tests must account for multiple testing, and standard statistical methods like multidimensional scaling and cluster analysis must be used on very large matrices, matrices that may be too big even fit into a computers memory.
Use of microarray methodologies will grow exponentially over the next few years as signal extraction and other aspects of the technology mature. Statistical analysis methods that extend a readily available statistical analysis package such as S-PLUS and are also capable of handling
Statistical analysis software
Baysian Modeling and Data Analysis in S-PLUS
RES900 – NIH “S-Plus Bayes II”
The ultimate goal of this project is to provide an extensive suite of Bayesian statistical software tools, utilizing a fast and effective Markov chain Monte Carlo (MCMC) computation engine. The overall implementation will be in the S-PLUS object-oriented language and system for statistical modeling and data analysis, and the MCMC engine will be implemented in C or C++, with an efficient interface to S-PLUS. The implementation will emphasize an ease-of-use paradigm that strongly encourages routine use of Bayesian methods as well as research-oriented exploration for new Statistical techniques by S-PLUS. We will develop Bayesian methods for the most widely used statistical models such as a hierarchical linear regression models, generalized linear mixed models, missing data models and models for robust inference. A large percentage of statisticians in the United States are employed in biostatistics and allied “bio” industries, and a considerable amount of statistical education and research occurs in medical and health related fields. The availability of a broad range of Bayesian statistical methods in a commercially viable data analysis product such as S-PLUS, will provide an important service to these industries, and to the research and educational needs by supporting and advancing the emerging paradigm of Bayesian modeling and data analysis.
Statistical analysis software
The ultimate goal of this project is to provide an extensive suite of Bayesian statistical software tools, utilizing a fast and effective Markov chain Monte Carlo (MCMC) computation engine. The overall implementation will be in the S-PLUS object-oriented language and system for statistical modeling and data analysis, and the MCMC engine will be implemented in C or C++, with an efficient interface to S-PLUS. The implementation will emphasize an ease-of-use paradigm that strongly encourages routine use of Bayesian methods as well as research-oriented exploration for new Statistical techniques by S-PLUS. We will develop Bayesian methods for the most widely used statistical models such as a hierarchical linear regression models, generalized linear mixed models, missing data models and models for robust inference. A large percentage of statisticians in the United States are employed in biostatistics and allied “bio” industries, and a considerable amount of statistical education and research occurs in medical and health related fields. The availability of a broad range of Bayesian statistical methods in a commercially viable data analysis product such as S-PLUS, will provide an important service to these industries, and to the research and educational needs by supporting and advancing the emerging paradigm of Bayesian modeling and data analysis.
Statistical analysis software
Bootstrap Tilting Inference and Large Data Sets
RES850 – NSF “Tilting II”
Phase I project intends to develop user friendly component software for classical econometric estimation and inference based on simulation methods. In the last decade different simulation-based methods have been developed to tackle complex economic/statistical models which cannot be estimated by conventional methods such as MLE and GMM. Although these simulation-based estimators have desirable theoretical properties, they have remained to be research topics in academia and have not become useful tools for practitioners because of lack user friendly software. We plan to study three leading applications for simulation-based methods: multinomial probit model for cross-sectional data, multiperiod multinomial probit model for panel data, and stochastic volatility models for time series data. We will use extensive Monte Carlo experiments to explore finite sample properties of various aspects of estimation and inference, with an aim of improving and stabilizing the current algorithms. The user friendly component software will be developed using the state-of-art JavaBean technology and provide intuitive graphical user interface.
The JavaBeans will also be supplied as S-PLUS functions to gain a broad user base. The software will help worldwide economists and practitioners in other fields such as financial industry, social sciences, and biotechnology to conduct flexible and extensible model estimation and inference.
Statistical analysis software
Phase I project intends to develop user friendly component software for classical econometric estimation and inference based on simulation methods. In the last decade different simulation-based methods have been developed to tackle complex economic/statistical models which cannot be estimated by conventional methods such as MLE and GMM. Although these simulation-based estimators have desirable theoretical properties, they have remained to be research topics in academia and have not become useful tools for practitioners because of lack user friendly software. We plan to study three leading applications for simulation-based methods: multinomial probit model for cross-sectional data, multiperiod multinomial probit model for panel data, and stochastic volatility models for time series data. We will use extensive Monte Carlo experiments to explore finite sample properties of various aspects of estimation and inference, with an aim of improving and stabilizing the current algorithms. The user friendly component software will be developed using the state-of-art JavaBean technology and provide intuitive graphical user interface.
The JavaBeans will also be supplied as S-PLUS functions to gain a broad user base. The software will help worldwide economists and practitioners in other fields such as financial industry, social sciences, and biotechnology to conduct flexible and extensible model estimation and inference.
Statistical analysis software
Efficient Statistical Algorithms for Dropout Data
RES840 – NIH “Drop Out”
Missing and dropout data are common features in longitudinal studies. In many cases, the dropout process is related to the outcome process. This situation creates tremendous difficulties in analyzing such data. No commercial software currently considers the dropout mechanisms in dealing with non-random dropout. Consequently, the results are biased and misleading. The ultimate objective of our research is the development of S+Dropout: a software package for handling various dropout mechanisms. The research will simultaneously consider the dropout and the response processes. We will develop model-based approaches and hierarchical structures for testing the dropout mechanisms. Efficient EM algorithms and Gibbs sampling will be developed for fitting various models. Since these algorithms are relying heavily on the modeling assumptions of the uncollected data, the validity of the assumptions has to be verified in data analysis. To perform this investigation, we provide an analytic and graphic suite for sensitivity analysis. The S+Dropout module will be implemented as a module in the S-Plus language. A comprehensive case study guidebook will also be developed using real problems involving dropout data..
Statistical analysis software
Missing and dropout data are common features in longitudinal studies. In many cases, the dropout process is related to the outcome process. This situation creates tremendous difficulties in analyzing such data. No commercial software currently considers the dropout mechanisms in dealing with non-random dropout. Consequently, the results are biased and misleading. The ultimate objective of our research is the development of S+Dropout: a software package for handling various dropout mechanisms. The research will simultaneously consider the dropout and the response processes. We will develop model-based approaches and hierarchical structures for testing the dropout mechanisms. Efficient EM algorithms and Gibbs sampling will be developed for fitting various models. Since these algorithms are relying heavily on the modeling assumptions of the uncollected data, the validity of the assumptions has to be verified in data analysis. To perform this investigation, we provide an analytic and graphic suite for sensitivity analysis. The S+Dropout module will be implemented as a module in the S-Plus language. A comprehensive case study guidebook will also be developed using real problems involving dropout data..
Statistical analysis software
Wavelet-Based Analysis/Software for Multi-Scale Fractal
RES810 – Airforce “Fractal II”
To conduct research on the analysis of time series that are generated by non-stationary multi-fractal processes (examples of such series include atmospheric turbulence). Because the discrete wavelet transform is a natural tool for use with non-stationary and scale-dependent data, we propose to study estimators based upon this transform. These include wavelet-based approximate maximum likelihood and least squares estimators of fractionally differenced processes adapted to work effectively in the presence of (i) time-varying power laws, (ii) multi-scale fractal characteristics and (iii) large scale trends. Insightful intends to investigate the prediction (extrapolation) of non-stationary multi-fractal processes through a subband decomposition approach in which forecasts on each subband are generated using either stochastic or deterministic predictors and then recombined using the inverse discrete wavelet transform to create a forecast for the original time series. And also, to apply our methodology to data provided to use by our Air Force sponsors (e.g., weather radar data). We propose to create a commercial-grade set of C routines that will encompass all of the methodology that comes out of our research along with a comprehensive collection of other techniques for dealing with multi-scale fractal processes (e.g., rescaled range analysis, dispersional analysis and scaled windowed variance methods).
Statistical analysis software
To conduct research on the analysis of time series that are generated by non-stationary multi-fractal processes (examples of such series include atmospheric turbulence). Because the discrete wavelet transform is a natural tool for use with non-stationary and scale-dependent data, we propose to study estimators based upon this transform. These include wavelet-based approximate maximum likelihood and least squares estimators of fractionally differenced processes adapted to work effectively in the presence of (i) time-varying power laws, (ii) multi-scale fractal characteristics and (iii) large scale trends. Insightful intends to investigate the prediction (extrapolation) of non-stationary multi-fractal processes through a subband decomposition approach in which forecasts on each subband are generated using either stochastic or deterministic predictors and then recombined using the inverse discrete wavelet transform to create a forecast for the original time series. And also, to apply our methodology to data provided to use by our Air Force sponsors (e.g., weather radar data). We propose to create a commercial-grade set of C routines that will encompass all of the methodology that comes out of our research along with a comprehensive collection of other techniques for dealing with multi-scale fractal processes (e.g., rescaled range analysis, dispersional analysis and scaled windowed variance methods).
Statistical analysis software
An Inverse Inference Engine for high Precision Web Search
RES790 – DARPA “Inverse II”
The Phase I work has proved the precision and scalability of the inverse inference algorithm, and its ability to perform latent semantic analysis. In Phase II, we will extend the functionality of the algorithm to encompass cross-language document retrieval, tracking of document clusters in time, and fast hierarchical clustering of large document databases. The indexing structure will evolve from an information matrix to an information tensor. The information tensor will accommodate multidimensional term attributes like work position, part of speech, and taxonomical and syntactic tags. We will embed this richer indexing structure and all search functionality in the Oracle interMedia cartridge. New query operators will provide support for word n-grams, ordered phrases, term broadening, cross document entity tracking and extraction of entity relationships. We will also improve the performance of the soft hyperlink navigation tool. We will validate the precision of our search technology by participating in the TREC and CLEF competitions on a regular basis throughout the duration of the contract.
Statistical analysis software
The Phase I work has proved the precision and scalability of the inverse inference algorithm, and its ability to perform latent semantic analysis. In Phase II, we will extend the functionality of the algorithm to encompass cross-language document retrieval, tracking of document clusters in time, and fast hierarchical clustering of large document databases. The indexing structure will evolve from an information matrix to an information tensor. The information tensor will accommodate multidimensional term attributes like work position, part of speech, and taxonomical and syntactic tags. We will embed this richer indexing structure and all search functionality in the Oracle interMedia cartridge. New query operators will provide support for word n-grams, ordered phrases, term broadening, cross document entity tracking and extraction of entity relationships. We will also improve the performance of the soft hyperlink navigation tool. We will validate the precision of our search technology by participating in the TREC and CLEF competitions on a regular basis throughout the duration of the contract.
Statistical analysis software
Thursday, September 11, 2008
Tree-based Software for Biomedical Applications
We propose to develop software for tree-based methods which implements research on biomedical applications not currently supported by commercial software. Examples include survival and longitudinal data analysis, clustering, and generalized tree models. Tree-based tools offer many advantages including easy interpretation. Tree representations may mimic the way that many medical scientists think about data. Trees are also relatively fast to build and search, making them suitable for interactive clustering of large data sets such as encountered in image analysis.
Phase II research will develop algorithms, model selection strategies, and stability diagnostics for tree based methods. Dynamic, interactive graphical tools will enable the user to explore the resulting tree-based model, and interpret structure in data by linking the tree representation to a variety of graphical and analytical tools.
We will create software that analysts find flexible and easy to use, enabling medical researchers to use tree-based tools to explore and understand data from a wide variety of applications. Additionally, the software will be supported in an integrated environment for data analysis, and permit analysts, consultants, and statistical researchers to extend the software to incorporate future innovations in recursive partitioning research.
Statistical analysis software
Phase II research will develop algorithms, model selection strategies, and stability diagnostics for tree based methods. Dynamic, interactive graphical tools will enable the user to explore the resulting tree-based model, and interpret structure in data by linking the tree representation to a variety of graphical and analytical tools.
We will create software that analysts find flexible and easy to use, enabling medical researchers to use tree-based tools to explore and understand data from a wide variety of applications. Additionally, the software will be supported in an integrated environment for data analysis, and permit analysts, consultants, and statistical researchers to extend the software to incorporate future innovations in recursive partitioning research.
Statistical analysis software
Mendelian Model Based Inference in Statistical Genetics
RES730 – NIH “Genetics”
Phase I research will focus on designing a single S-Plus software package that facilitates use of statistical techniques for the genetics community. In genetics, research is conducted in order to test hypotheses, which are derived from the genetic theory. Having stated a specific hypothesis which seem important to a certain theory, data is collected which should enable the researcher to make a decision concerning the hypothesis. The decision may lead researchers to retain, revise, or reject the hypothesis. To reach an objective decision the researcher must have an objective procedure for rejecting or accepting that hypothesis. This objective procedure should be based on information or data obtained in research. The procedure usually involves several steps. Despite the vast amount of genetic statistical procedures and software that exist to solve problems there is not existing software package that provides comprehensive and easy to use genetic statistical functions for the biomedical researcher. The genetics epidemiology module will include descriptive and exploratory data analysis, methods testing hypotheses about genotype frequencies, random/non-randomness of data, exact tests for independence, computation of likelihoods for genetic models.
Statistical analysis software
Phase I research will focus on designing a single S-Plus software package that facilitates use of statistical techniques for the genetics community. In genetics, research is conducted in order to test hypotheses, which are derived from the genetic theory. Having stated a specific hypothesis which seem important to a certain theory, data is collected which should enable the researcher to make a decision concerning the hypothesis. The decision may lead researchers to retain, revise, or reject the hypothesis. To reach an objective decision the researcher must have an objective procedure for rejecting or accepting that hypothesis. This objective procedure should be based on information or data obtained in research. The procedure usually involves several steps. Despite the vast amount of genetic statistical procedures and software that exist to solve problems there is not existing software package that provides comprehensive and easy to use genetic statistical functions for the biomedical researcher. The genetics epidemiology module will include descriptive and exploratory data analysis, methods testing hypotheses about genotype frequencies, random/non-randomness of data, exact tests for independence, computation of likelihoods for genetic models.
Statistical analysis software
Visible Human Project Image Processing Tools
RES710 – NLM “VHTK”
The underlying basis of the Visible Human Project (VHP) is a set of three-dimensional high-resolution digital image data of human cadavers in multiple image modalities. An impediment to the advancement and dissemination of research using these three-dimensional, stacked image data is the lack of definition and availability of a common, basic software infrastructure for registration and segmentation.
The research intends to develop requirements for segmentation and registration infrastructure through collaborative definition of a taxonomy of existing methodology and algorithms. We will then design and implement part of the infrastructure based on the defined requirements, and develop demonstration applications for segmentation and registration of VHP data.
In addition, Insightful intends to serve in the role of systems integrator in this collaborative effort. We will maintain a repository for the code base and documentation; maintain a web site for inter-collaborator and –contractor communication; provide standards for uniformity of code, documentation, and tests; perform integration testing; and perform release engineering tasks such as the systematic and periodic building and release of the software.
This will result in a common, basic software framework for the advancement and dissemination of research and the development of applications using the VHP and other stacked image data.
Statistical analysis software
The underlying basis of the Visible Human Project (VHP) is a set of three-dimensional high-resolution digital image data of human cadavers in multiple image modalities. An impediment to the advancement and dissemination of research using these three-dimensional, stacked image data is the lack of definition and availability of a common, basic software infrastructure for registration and segmentation.
The research intends to develop requirements for segmentation and registration infrastructure through collaborative definition of a taxonomy of existing methodology and algorithms. We will then design and implement part of the infrastructure based on the defined requirements, and develop demonstration applications for segmentation and registration of VHP data.
In addition, Insightful intends to serve in the role of systems integrator in this collaborative effort. We will maintain a repository for the code base and documentation; maintain a web site for inter-collaborator and –contractor communication; provide standards for uniformity of code, documentation, and tests; perform integration testing; and perform release engineering tasks such as the systematic and periodic building and release of the software.
This will result in a common, basic software framework for the advancement and dissemination of research and the development of applications using the VHP and other stacked image data.
Statistical analysis software
A Nonparametric MLE Survival Analysis Module
RES690 – NIH “Censor II”
Censored and truncated data frequently arise from HIV/AIDS related and other clinical trials and observational studies. Advanced nonparametric survival analysis techniques are required to handle these complicated incomplete data without sacrificing modeling principles.
This project develops a usable software module based on recent advances in survival analysis that are routinely applicable to these incomplete data. The software module includes the following innovative estimation techniques: (1) nonparametric maximum likelihood estimator (NPMLE) for survival functions with interval censored, doubly censored and truncated data; (2) maximum profile likelihood approach to the proportional hazard model with interval censored and doubly censored data; and (3) implementation in modern statistical computing environment.
The software module complements its estimation techniques with the following inference procedures: (1) Nonparametric bootstrap, semiparametric likelihood ratio based confidence intervals and bands, (2) Rao, Wald and likelihood ratio tests and confidence sets in the proportional hazards model by profile likelihood. The feasibility of the project rests on several foundations, some of which consolidated and extended in the Phase I research: (1) a significantly faster hybrid algorithm for computing the NPMLE; (2) an effective maximization technique for computing the maximum profile likelihood estimates; (3) an object-oriented data analysis and graphics software environment S-PLUS to hose these techniques.
Statistical analysis software
Censored and truncated data frequently arise from HIV/AIDS related and other clinical trials and observational studies. Advanced nonparametric survival analysis techniques are required to handle these complicated incomplete data without sacrificing modeling principles.
This project develops a usable software module based on recent advances in survival analysis that are routinely applicable to these incomplete data. The software module includes the following innovative estimation techniques: (1) nonparametric maximum likelihood estimator (NPMLE) for survival functions with interval censored, doubly censored and truncated data; (2) maximum profile likelihood approach to the proportional hazard model with interval censored and doubly censored data; and (3) implementation in modern statistical computing environment.
The software module complements its estimation techniques with the following inference procedures: (1) Nonparametric bootstrap, semiparametric likelihood ratio based confidence intervals and bands, (2) Rao, Wald and likelihood ratio tests and confidence sets in the proportional hazards model by profile likelihood. The feasibility of the project rests on several foundations, some of which consolidated and extended in the Phase I research: (1) a significantly faster hybrid algorithm for computing the NPMLE; (2) an effective maximization technique for computing the maximum profile likelihood estimates; (3) an object-oriented data analysis and graphics software environment S-PLUS to hose these techniques.
Statistical analysis software
Analysis of Longitudinal Data with Serial Correlations
RES620 – NIH “Longitudual II”
The ultimate objective of our research is the development of S+LONGIST: a next generation software toolkit for the longitudinal studies. This research will make a fundamental contribution to the conduct of public health studies by developing coherent and mature methodology and software for handling serial correlation in longitudinal data. Considerable research has been devoted towards developing the necessary methodology for applying mixed-effects models and estimating equations models. In spite of this research, longitudinal data analysts often do not fully account for the effects of serial correlation. The aim of the proposed research is to overcome the obstacles and extend the benefits of the research performed to a much wider audience of biomedical analysts and practitioners. To achieve this aim, a framework will be developed based upon three approaches: a state space method, an approximate likelihood approach, and a Quasi-likelihood approach to fit longitudinal data with errors from an exponential family distribution. This methodology will be matured by incorporating diagnostic techniques and will be implemented as an object-oriented software module in the S-Plus language. A comprehensive case study guidebook will be developed involving real problems with serially correlated data.
Statistical analysis software
The ultimate objective of our research is the development of S+LONGIST: a next generation software toolkit for the longitudinal studies. This research will make a fundamental contribution to the conduct of public health studies by developing coherent and mature methodology and software for handling serial correlation in longitudinal data. Considerable research has been devoted towards developing the necessary methodology for applying mixed-effects models and estimating equations models. In spite of this research, longitudinal data analysts often do not fully account for the effects of serial correlation. The aim of the proposed research is to overcome the obstacles and extend the benefits of the research performed to a much wider audience of biomedical analysts and practitioners. To achieve this aim, a framework will be developed based upon three approaches: a state space method, an approximate likelihood approach, and a Quasi-likelihood approach to fit longitudinal data with errors from an exponential family distribution. This methodology will be matured by incorporating diagnostic techniques and will be implemented as an object-oriented software module in the S-Plus language. A comprehensive case study guidebook will be developed involving real problems with serially correlated data.
Statistical analysis software
Useable Robust Statistical Modeling Inference
RES570 – NIH “Robust II”
Data arising in medical, biomedical and biotechnical research and analysis often contain outliers, in different forms and from diverse causes. The outliers may for example occur independently or in sub-population groups, and may be due to extraordinary responses of individuals or sub-groups of individuals in a study, or be due to sporadic instrument or recording errors. Whatever the source and form of outliners, they can and often do have very adverse effects on classical statistical modeling methods such as least squares fitting of linear models, analysis of variance, logistic regression, generalized linear models, survival analysis, and covariance matrix estimation.
A primary goal of the research is to provide theoretical justifications and software for a very broad range of robust modeling and analysis methods which provide a good fit to the bulk of the data in the presence of outliers, enable rapid identification of outliers, and provide good statistical inference results. The software will be implemented in the S-PLUS object-oriented environment for data analysis, statistical modeling and graphics, and will emphasize ease of use for both “power” users who are comfortable with command line use of an object-oriented language, and for a very broad range of users who require a well-designed graphical user interface (GUI).
Statistical analysis software
Data arising in medical, biomedical and biotechnical research and analysis often contain outliers, in different forms and from diverse causes. The outliers may for example occur independently or in sub-population groups, and may be due to extraordinary responses of individuals or sub-groups of individuals in a study, or be due to sporadic instrument or recording errors. Whatever the source and form of outliners, they can and often do have very adverse effects on classical statistical modeling methods such as least squares fitting of linear models, analysis of variance, logistic regression, generalized linear models, survival analysis, and covariance matrix estimation.
A primary goal of the research is to provide theoretical justifications and software for a very broad range of robust modeling and analysis methods which provide a good fit to the bulk of the data in the presence of outliers, enable rapid identification of outliers, and provide good statistical inference results. The software will be implemented in the S-PLUS object-oriented environment for data analysis, statistical modeling and graphics, and will emphasize ease of use for both “power” users who are comfortable with command line use of an object-oriented language, and for a very broad range of users who require a well-designed graphical user interface (GUI).
Statistical analysis software
Mixed Effects Multidimensional Scaling
RES490 – NIH “Indscal II”
Multidimensional scaling (MDS) is a psychometric method with wide application in behavioral science research. The purpose here is to develop software for a new class of MDS models. In these new models parameters associated with individuals are modeled as random effects rather than as fixed parameters. For the diagonal metric (or INDSCAL) models, these parameters are the subject weights. The resulting random effects MDS model has many advantages over its classical counterpart. For example, we are better able to estimate subject weights even when only one dissimilarity is observed on an individual, and we can make model-based inferences about the sampled population of subject weights.
The plan is to develop a comprehensive module of computational algorithms for computing estimates in this new class of MDS models. Included in this module will be software for model fitting, inference, diagnostics, and other appropriate statistical techniques, a graphical user interface, a users manual, and online documentation. The software will also contain procedures for robust estimation.
Statistical analysis software
Multidimensional scaling (MDS) is a psychometric method with wide application in behavioral science research. The purpose here is to develop software for a new class of MDS models. In these new models parameters associated with individuals are modeled as random effects rather than as fixed parameters. For the diagonal metric (or INDSCAL) models, these parameters are the subject weights. The resulting random effects MDS model has many advantages over its classical counterpart. For example, we are better able to estimate subject weights even when only one dissimilarity is observed on an individual, and we can make model-based inferences about the sampled population of subject weights.
The plan is to develop a comprehensive module of computational algorithms for computing estimates in this new class of MDS models. Included in this module will be software for model fitting, inference, diagnostics, and other appropriate statistical techniques, a graphical user interface, a users manual, and online documentation. The software will also contain procedures for robust estimation.
Statistical analysis software
Tuesday, September 9, 2008
B-Spline Statistical Technologies and Hazard Regression
RES420 – NIH “HARE II”
Event history analysis methods are widely used in medical research: for example in clinical trials, in pharmacological studies, in studying the effects of ionizing radiation, in screening for breast and colorectal cancer, and in the study of sexually transmitted diseases. Recently there has been considerable interest in the biostatistics literature regarding the use of splines in estimating the log-hazard function. The purpose of the research is to incorporate recent and ongoing advances in this area into HARE, a product utilizing adaptive polynomial spline technology for the analysis of event history data.
When the observed failure times are generated by an unknown mechanism, interest often lies in estimating a hazard, survival, or density function. In HARE these functions are estimated by means of polynomial splines and their selected tensor products in univariate and multivariate survival models with covariates. MARS-like methods are used to adaptively select the spline basis functions. HARE will also compute standard test statistics, provide an easy-to-use graphical user interface with extensive guidance capabilities, and comprehensively graphically display the computed estimates and diagnostics.
Statistical analysis software
Event history analysis methods are widely used in medical research: for example in clinical trials, in pharmacological studies, in studying the effects of ionizing radiation, in screening for breast and colorectal cancer, and in the study of sexually transmitted diseases. Recently there has been considerable interest in the biostatistics literature regarding the use of splines in estimating the log-hazard function. The purpose of the research is to incorporate recent and ongoing advances in this area into HARE, a product utilizing adaptive polynomial spline technology for the analysis of event history data.
When the observed failure times are generated by an unknown mechanism, interest often lies in estimating a hazard, survival, or density function. In HARE these functions are estimated by means of polynomial splines and their selected tensor products in univariate and multivariate survival models with covariates. MARS-like methods are used to adaptively select the spline basis functions. HARE will also compute standard test statistics, provide an easy-to-use graphical user interface with extensive guidance capabilities, and comprehensively graphically display the computed estimates and diagnostics.
Statistical analysis software
Statistical Software for Resampling Methods
RES300 – NIH “Resample II”
Medical data often require complex models. For example, clinical monitoring produces time induced correlation, and relationships among variables change due to medical intervention. Many popularly used biostatistical procedures depend on approximations made for mathematical tractability.
Resampling methods extend the range of classical methods, and have the potential to dramatically affect 21st Century statistics. Resampling methods approximate the distribution of a statistic using only the observed data. The two-fold advantages of resampling methods are that (1) they are conceptually simple and (2) they often apply in complex problems inaccessible through other techniques.
Phase II research will develop algorithms, graphics, and diagnostics for several resampling methods, focusing on the bootstrap. Graphics and a graphical user interface will make the software easy to learn and use. Research will extend and combine efficient computational techniques. The software will support the different needs of (1) data analysts and (2) biostatistical researchers who want to modify resampling capabilities.
S-Plus Resample will enable medical researchers to earn a greater return on their investment of collecting data: achieving reliable inference with computational techniques that are easy to understand and use, yet apply in complex problems in accessible through other methods.
Statistical analysis software
Medical data often require complex models. For example, clinical monitoring produces time induced correlation, and relationships among variables change due to medical intervention. Many popularly used biostatistical procedures depend on approximations made for mathematical tractability.
Resampling methods extend the range of classical methods, and have the potential to dramatically affect 21st Century statistics. Resampling methods approximate the distribution of a statistic using only the observed data. The two-fold advantages of resampling methods are that (1) they are conceptually simple and (2) they often apply in complex problems inaccessible through other techniques.
Phase II research will develop algorithms, graphics, and diagnostics for several resampling methods, focusing on the bootstrap. Graphics and a graphical user interface will make the software easy to learn and use. Research will extend and combine efficient computational techniques. The software will support the different needs of (1) data analysts and (2) biostatistical researchers who want to modify resampling capabilities.
S-Plus Resample will enable medical researchers to earn a greater return on their investment of collecting data: achieving reliable inference with computational techniques that are easy to understand and use, yet apply in complex problems in accessible through other methods.
Statistical analysis software
S+Proteome
An S-PLUS Module for Protein Mass Spectra Processing and Classification
This project is funded in part with Federal SBIR funds from NCI under contract No. HHSN261200533002C and HHSN261200533003C. The goal is to develop advanced bioinformatics software for analysis and evaluation of proteomic cellular signatures in cancer prevention research. Biomarker discovery for early cancer detection constitutes one of the most important fields in cancer research. Traditional biomarker discovery approaches yield limited sensitivity and specificity in cancer detection. Recent quests to achieve reliable biomarker identification have led researchers to protein analysis (proteomics) via mass spectrometry (MS). Mass spectral signature analysis involves two key steps: processing and classification.
In the first contract, we are addressing the challenges in preprocessing MS data. Recent studies have identified multiple technical and biological shortcomings, resulting in varied forms of noise that must be identified and rectified prior to meaningful use of the data. Insightful's proposed solutions include mass calibration, denoising, baseline detrending, intensity normalization, peak detection, peak alignment, peak quantification, and feature selection and extraction. In the second contract, we are addressing the challenges in classification of high-dimensional MS data to differentiate disease states. Insightful's proposed solutions include a comprehensive suite of cutting-edge classification tools, enabling researchers to explore different techniques, predict classification accuracy, and interpret results in a biologically meaningful way.
The combined software will provide a comprehensive set of tools to assess data quality and state-of-the-art graphical display tools. In addition, we are developing a companion casebook of selected mass spectra data and applications. The software's modular design and extendible structure will facilitate: rigorous validation of promising approaches on extensive datasets; further development of novel bioinformatical techniques; and application of validated biomarker identification disease screening tests.
These projects complement Insightful's S+ArrayAnalyze module for rigorous statistical analysis of microarray data, interactive graphical reporting, automated annotation, publication quality output, and an extensible development environment.
Statistical analysis software
This project is funded in part with Federal SBIR funds from NCI under contract No. HHSN261200533002C and HHSN261200533003C. The goal is to develop advanced bioinformatics software for analysis and evaluation of proteomic cellular signatures in cancer prevention research. Biomarker discovery for early cancer detection constitutes one of the most important fields in cancer research. Traditional biomarker discovery approaches yield limited sensitivity and specificity in cancer detection. Recent quests to achieve reliable biomarker identification have led researchers to protein analysis (proteomics) via mass spectrometry (MS). Mass spectral signature analysis involves two key steps: processing and classification.
In the first contract, we are addressing the challenges in preprocessing MS data. Recent studies have identified multiple technical and biological shortcomings, resulting in varied forms of noise that must be identified and rectified prior to meaningful use of the data. Insightful's proposed solutions include mass calibration, denoising, baseline detrending, intensity normalization, peak detection, peak alignment, peak quantification, and feature selection and extraction. In the second contract, we are addressing the challenges in classification of high-dimensional MS data to differentiate disease states. Insightful's proposed solutions include a comprehensive suite of cutting-edge classification tools, enabling researchers to explore different techniques, predict classification accuracy, and interpret results in a biologically meaningful way.
The combined software will provide a comprehensive set of tools to assess data quality and state-of-the-art graphical display tools. In addition, we are developing a companion casebook of selected mass spectra data and applications. The software's modular design and extendible structure will facilitate: rigorous validation of promising approaches on extensive datasets; further development of novel bioinformatical techniques; and application of validated biomarker identification disease screening tests.
These projects complement Insightful's S+ArrayAnalyze module for rigorous statistical analysis of microarray data, interactive graphical reporting, automated annotation, publication quality output, and an extensible development environment.
Statistical analysis software
Insightful Research
Insightful Research provides contract research services for clients seeking top experts for cutting-edge analytical projects. Our large research group keeps Insightful technology and services at the forefront of industry, with projects including genomics, image analysis, signal processing and information retrieval. Our clients range from The National Institutes of Health to the US Department of Defense. In each case we are selected because of our extraordinary expertise and analytical software. Here are just some of our projects:
Research Projects
* S+Proteome
An S-PLUS Module for Protein Mass Spectra Processing and Classification...
* Statistical Software for Resampling Methods
Medical data often require complex models. For example, clinical monitoring produces time induced correlation, and relationships among variables...
* B-Spline Statistical Technologies and Hazard Regression
Event history analysis methods are widely used in medical research: for example in clinical trials, in pharmacological studies, in studying the...
* Mixed Effects Multidimensional Scaling
Multidimensional scaling (MDS) is a psychometric method with wide application in behavioral science research. The purpose here is to develop...
* Useable Robust Statistical Modeling Inference
Data arising in medical, biomedical and biotechnical research and analysis often contain outliers, in different forms and from diverse causes. The...
* Analysis of Longitudinal Data with Serial Correlations
The ultimate objective of our research is the development of S+LONGIST: a next generation software toolkit for the longitudinal studies. This...
* A Nonparametric MLE Survival Analysis Module
Censored and truncated data frequently arise from HIV/AIDS related and other clinical trials and observational studies. Advanced nonparametric...
* Visible Human Project Image Processing Tools
The underlying basis of the Visible Human Project (VHP) is a set of three-dimensional high-resolution digital image data of human cadavers in...
* Mendelian Model Based Inference in Statistical Genetics
Phase I research will focus on designing a single S-Plus software package that facilitates use of statistical techniques for the genetics community...
* Tree-Based Software for Biomedical Applications
The intention of the research is to develop software for tree-based methods which implements research on biomedical applications not currently...
* An Inverse Inference Engine for high Precision Web Search
The Phase I work has proved the precision and scalability of the inverse inference algorithm, and its ability to perform latent semantic analysis. ...
* Wavelet-Based Analysis/Software for Multi-Scale Fractal
To conduct research on the analysis of time series that are generated by non-stationary multi-fractal processes (examples of such series include...
* Efficient Statistical Algorithms for Dropout Data
Missing and dropout data are common features in longitudinal studies. In many cases, the dropout process is related to the outcome process. This...
* Bootstrap Tilting Inference and Large Data Sets
Phase I project intends to develop user friendly component software for classical econometric estimation and inference based on simulation methods. ...
* Baysian Modeling and Data Analysis in S-PLUS
The ultimate goal of this project is to provide an extensive suite of Bayesian statistical software tools, utilizing a fast and effective Markov...
* Exploratory Analysis of Gene Expression Data
Microarray’s are an exciting new technology allowing the simultaneous collection of gene expression data for literally thousands of genes. Recent...
Statistical analysis software
Research Projects
* S+Proteome
An S-PLUS Module for Protein Mass Spectra Processing and Classification...
* Statistical Software for Resampling Methods
Medical data often require complex models. For example, clinical monitoring produces time induced correlation, and relationships among variables...
* B-Spline Statistical Technologies and Hazard Regression
Event history analysis methods are widely used in medical research: for example in clinical trials, in pharmacological studies, in studying the...
* Mixed Effects Multidimensional Scaling
Multidimensional scaling (MDS) is a psychometric method with wide application in behavioral science research. The purpose here is to develop...
* Useable Robust Statistical Modeling Inference
Data arising in medical, biomedical and biotechnical research and analysis often contain outliers, in different forms and from diverse causes. The...
* Analysis of Longitudinal Data with Serial Correlations
The ultimate objective of our research is the development of S+LONGIST: a next generation software toolkit for the longitudinal studies. This...
* A Nonparametric MLE Survival Analysis Module
Censored and truncated data frequently arise from HIV/AIDS related and other clinical trials and observational studies. Advanced nonparametric...
* Visible Human Project Image Processing Tools
The underlying basis of the Visible Human Project (VHP) is a set of three-dimensional high-resolution digital image data of human cadavers in...
* Mendelian Model Based Inference in Statistical Genetics
Phase I research will focus on designing a single S-Plus software package that facilitates use of statistical techniques for the genetics community...
* Tree-Based Software for Biomedical Applications
The intention of the research is to develop software for tree-based methods which implements research on biomedical applications not currently...
* An Inverse Inference Engine for high Precision Web Search
The Phase I work has proved the precision and scalability of the inverse inference algorithm, and its ability to perform latent semantic analysis. ...
* Wavelet-Based Analysis/Software for Multi-Scale Fractal
To conduct research on the analysis of time series that are generated by non-stationary multi-fractal processes (examples of such series include...
* Efficient Statistical Algorithms for Dropout Data
Missing and dropout data are common features in longitudinal studies. In many cases, the dropout process is related to the outcome process. This...
* Bootstrap Tilting Inference and Large Data Sets
Phase I project intends to develop user friendly component software for classical econometric estimation and inference based on simulation methods. ...
* Baysian Modeling and Data Analysis in S-PLUS
The ultimate goal of this project is to provide an extensive suite of Bayesian statistical software tools, utilizing a fast and effective Markov...
* Exploratory Analysis of Gene Expression Data
Microarray’s are an exciting new technology allowing the simultaneous collection of gene expression data for literally thousands of genes. Recent...
Statistical analysis software
European Training Schedule
2008 Course Titles
S-PLUS Essentials
Analysis of Financial Time Series
S-PLUS Essentials
Statistical Models in Finance with S-PLUS
S-PLUS Essentials
S-PLUS Essentials
Statistical analysis software
S-PLUS Essentials
Dates: 23-25 Spet
Location: Basingstoke
Location: Basingstoke
Register: Insightful UK
Analysis of Financial Time Series
Dates: 7-8 Oct
Location: Basel
Location: Basel
Register: Insightful Switzerland
S-PLUS Essentials
Dates: 21-23 Oct
Location: Toulouse
Location: Toulouse
Register: Insightful France
Statistical Models in Finance with S-PLUS
Dates: 4 Nov
Location: Frankfurt
Location: Frankfurt
Register: Insightful Switzerland
S-PLUS Essentials
Dates: 11-13 Nov
Location: Basingstoke
Location: Basingstoke
Register: Insightful UK
S-PLUS Essentials
Dates: 11-13 Nov
Location: Basel
Location: Basel
Register: Insightful Switzerland
Statistical analysis software
Training Schedule
Schedule Overview
Whether you’re a beginner or seasoned professional, our international training services can help you improve your analytic skills. We offer public and private courses to meet your specific training requirements. Learn about the latest trends, methods and more from industry gurus and academic leaders. Hands-on training courses offer you an opportunity to apply your knowledge. Register online for any of the public courses offered below or contact us to schedule a private training course. For information about course cancellations, please read our training policy.
US Courses
Course Title: Beyond Essentials: Programming in S-PLUS
Dates: Sept 23-25
Location: San Francisco, CA
Register
Statistical analysis software
Whether you’re a beginner or seasoned professional, our international training services can help you improve your analytic skills. We offer public and private courses to meet your specific training requirements. Learn about the latest trends, methods and more from industry gurus and academic leaders. Hands-on training courses offer you an opportunity to apply your knowledge. Register online for any of the public courses offered below or contact us to schedule a private training course. For information about course cancellations, please read our training policy.
US Courses
Course Title: Beyond Essentials: Programming in S-PLUS
Dates: Sept 23-25
Location: San Francisco, CA
Register
Statistical analysis software
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