Spring 2006 Meeting
Held on March 9,
2006 at the Wyndham Glenview Suites.
The Program consisted of four presentations:
- Modern Statistical Analysis Using Stata
Roberto Gutierrez, Ph.D., StataCorp LP
- Statistical and Graphical Analysis of Clinical and Observational
Data Using Splus
Michael O’Connell, Ph.D. Insightful Corporation
- Statistical Analysis Using SPSS
Ming-Long Lam, Ph.D. SPSS Inc.
- SAS Enterprise Miner, SAS Forecast Server, and Other SAS
Statistically Based Solutions
William T. Winand and Ross Bettinger
Modern Statistical Analysis Using Stata by
Roberto Gutierrez, Ph.D., StataCorp LP
Dr. Roberto Gutierrez received his PhD in
Statistics in 1995 from Texas A&M University. He is StataCorp's Director of
Statistics. His area of specialization is in survival analysis and mixed
Stata is a
widely-used general purpose statistical software package for data
management, statistical analysis, and graphics. Currently at version 9.1,
Stata is particularly suited for biostatisticians and epidemiologists.
Besides the standard statistical routines such as linear regression, ANOVA,
and regression models for limited dependent variables, included in Stata are
tools for fitting linear mixed models, analyzing epidemiological tables, and
survival analysis. Stata also possesses an easy-to-use GUI interface, the
ability to produce publication-quality graphics, and is based upon a
fully-integrated programming environment that includes Mata, a matrix
programming language. This discussion will look at some of the Stata
capabilities for modern statistical analysis.
Statistical and Graphical Analysis of Clinical and Observational Data
Using S-plus by Michael O’Connell, Ph.D. Insightful Corporation
Dr. Michael O'Connell has been working in the
medical device, diagnostics, pharmaceutical, and biotech arena for the past
15 years. He has recently concentrated his efforts in two areas: statistical
analysis of safety data and statistical analysis of microarray data. In both
of these areas, O’Connell is involved in the design and development of tools
for analysis and reporting of clinical and discovery data from S-PLUS. Dr.
O'Connell, Director of Life Science Solutions for Insightful Corporation,
has been also named one of the “100 Most Inspiring People in the Life
Sciences Industry” by the readers of PharmaVOICE magazine. Dr. O'Connell
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.
randomized, double-blind clinical trials enable inference on causality.
Retrospective observational studies, e.g. using medical claims data, have a
number of limitations compared with randomized prospective clinical trials,
most notably with respect to balance in known and unknown prognostic factors
which may cause a preference for one treatment over another. This talk
provides examples of statistical and graphical analysis of clinical trial
and observational data. The clinical examples include interactive graphical
review of results with end-users, graphics in formal clinical study reports,
and comparisons of adverse event data using Bayesian hierarchical models.
The observational examples include comparison of length of stay for patients
receiving new and standard therapy for treatment of a variety of infection
conditions using medical claims data from over 500 hospitals. This analysis
uses logistic regression and propensity
scoring; and includes 2-sample and survival curve comparisons, combined with
graphical analysis before and after the matching, assessing covariate
balance and treatment effects on length of stay.
The examples feature
the use of S-PLUS Trellis graphics using multiple sources of data and
metadata; and the S+Graphlet® technology with S-PLUS Server for interactive
graphical analysis and data browsing. S-PLUS provides an extensive set of
tools for statistical and graphical analysis of clinical and observational
Statistical Analysis Using SPSS by Ming-Long
Lam, Ph.D. SPSS Inc.
Dr. Ming-Long Lam is currently the
manager of Statistics Research and Master Statistician at SPSS Inc. in
Chicago. Ever since he joined SPSS in 1992, he led development of many
statistical procedures for analyzing the General Linear Models, the Variance
Component Models, the Mixed Effects Models, the Multinomial Logistic Models,
the Ordinal Regression Models, the Two-Step Cluster Model, the Complex
Samples module, and the forthcoming Generalized Linear Model. Besides his
work at SPSS Inc, he and a co-author will publish the book "Using Data
Analysis to Improve Student Learning: Toward 100% Proficiency". This book
will be available in September 2006. He received his Ph.D. degree in
Statistics from the University of Chicago.
Recent versions of SPSS were released
with a significant improvement in features that enhance the process of
performing statistical analyses, enrich the presentation of results, and
provide new or improved analytical procedures. This discussion will
highlight features that may impact the work of statisticians in enhancing
various statistical analyses, both simple and complex. Some examples and
applications will be drawn from mixed models, multinomial logistic models,
and programmability feature. A sneak preview of the forthcoming generalized
linear model will be given too.
SAS Enterprise Miner, SAS Forecast Server, and Other SAS Statistically
Based Solutions by William T. Winand and Ross Bettinger
Mr. William T. Winand is a Senior
Systems Engineer at SAS. William is Spearhead for Analytical Intelligence
within SAS' Health & Life Sciences Organization. In this role, William
consults with pharmaceutical, biotechnology, medical device, and health
insurance companies to identify areas where analytics can provide
significant business value and to demonstrate the capabilities of SAS
Enterprise Miner, SAS Forecast Server and other SAS statistically based
solutions to deliver that value. William has worked for SAS since 1995.
William also holds a Masters of Management degree from the Kellogg Graduate
School of Management, Northwestern University.
Mr. Ross Bettinger is an Analytical Consultant at SAS Institute. He
obtained his B.A. degree in Mathematics from UCLA, and Master’s degrees in
Systems Engineering from UCLA, Business/Statistics from University of
Wisconsin, Madison, and Electrical Engineering from Northeastern University.
He has over twenty-plus years of SAS® experience and eleven years of
statistical modeling experience. His area of expertise is in statistical
analysis, forecasting, data mining, and text mining. Mr. Bettinger has been
involved in various consulting projects including credit card risk
management, modeling in direct marketing, financial analysis and
forecasting, text mining for customer retention modeling in banking, and
warranty analysis. Some specific research questions that Mr. Bettinger has
been involved include work in credit card risk management settings to
identify key events that signal a card member’s likelihood to default on the
card’s remaining balance and bad debt, developing neural network models for
cross-sell and up-sell modeling for a credit card issuer, writing
specialized software that substantially reduced the scoring time required
for very large volumes of records, modeling card members’ spending patterns
and propensity to run up a high balance, building time series models of
future levels of bad debt, and analyzing call center data to determine if
there were specific words associated with accountholder attrition. Prior to
joining SAS, Mr. Bettinger worked as an Analytical Consultant for Epsilon
Data Management, Sears, Roebuck & Co., and Magnify, Inc. where he built
models for up-sell and cross-sell campaigns, catalog sales, insurance
underwriting, and credit card risk management.
mining is the process of data selection, exploration and building models
using vast data stores to uncover previously unknown patterns. What does
this mean to you, and what is its value your organization? With
organizational data growing exponentially, data mining is now a necessary
tool to shorten time to discovery and to provide new insights. SAS
Enterprise Miner, the industry leading data mining solution, streamlines the
data mining process to create highly accurate
predictive and descriptive models based on analysis of vast amounts of data
from across an enterprise. Examples from different areas of the Health and
Life Sciences organizations will be used to demonstrate the capabilities and
value of SAS Enterprise Miner.