Spring 2010 Meeting
Held on March 11, 2010 at the Crowne Plaza Hotel in Northbrook, IL. For registration information, please Click Here.
The Program consisted of three presentations:
- Gatekeeping Procedures
Ajit Tamhane, Ph.D., Senior Associate Dean & Professor, Northwestern University
- Power Analysis for Trials with Discrete Time Survival Endpoints
Mirjam Moerbeek, Ph.D., Professor, Utrecht University & Katarzyna Jozwiak, Ph.D. Candidate, Utrecht University, The Netherlands
-
Application of the Delta Method and Poisson Process on Relative Risk
Chihche Lin, Ph.D., Astellas Pharmacueticals
Gatkeeping Procedures by Ajit Tamhane, Ph.D., Northwestern University
Biographical Background
Ajit Tamhane is Senior Associate Dean of McCormick School of Engineering at
Northwestern University and Professor of Industrial Engineering & Management
Sciences (IEMS) with a courtesy appointment in the Department of Statistics. He
was Chairperson of the Department of IEMS from 2001 to 2008. He received Ph.D.
in Operations Research and Statistics from Cornell University and B.Tech. in
Mechanical Engineering from I.I.T. Bombay. He has been with Northwestern
University since 1975. Professor Tamhane has authored three books:
Multiple Comparison Procedures, with Yosef Hochberg (Wiley, 1987), Statistics
and Data Analysis with Dorothy Dunlop (Prentice Hall, 2000), and Statistical
Analysis of Designed Experiments (Wiley, 2009). He has edited two volumes of
collected papers and chapters: Design of Experiments: Ranking and Selection with
Tom Santner (Marcel-Dekker, 1984) and Multiple Testing Problems in
Pharmaceutical Statistics (Taylor & Francis, 2009) with Alex Dmitrienko and
Frank Bretz. He has published over 85 papers in the areas of multiple
comparisons, multiple testing problems in clinical trials, design of
experiments, chemometrics, quality control and clustering. His research is
funded by NIH and by NSA. He is a fellow of ASA.
Abstract
Gatekeeping procedures address the problems of testing hierarchically ordered
and logically related null hypotheses that arise in clinical trials involving
multiple endpoints, multiple doses, noninferiority-superiority tests, subgroup
analyses, etc. while controlling the familywise type I error rate. Because of
the practical importance of these problems, gatekeeping procedures have become
an active area of research in the last decade. This talk will trace the
developments in this field concluding with a summary of two current research
topics.
Power Analysis for Trials with Discrete Time Survival Endpoints by Mirjam Moerbeek, Ph.D., and Katarzyna Jozwiak, Utrecht University, the Netherlands
Biographical Background
Dr. Mirjam Moerbeek is associate professor at the department of Methods
and Statistics, Utrecht University, the Netherlands. She specializes in
statistical power analysis and optimal design, in particular for trials with
hierarchical or longitudinal data, such as cluster randomized trials. She
has received various research grants from the Netherlands Organization for
Scientific Research and is supervisor of a project on "Improving statistical
power analysis for trials on event occurrence by using an optimal design".
She currently supervises three Ph.D. students.
Katarzyna (Kasia) Jozwiak is a Ph.D. student at the department of Methods and Statistics, Utrecht University, the Netherlands. She studies optimal designs for trials with survival endpoints that are measured in discrete time. Relevant design issues are the optimal number of subjects, the optimal number of measurements and the optimal duration of the trial.
Abstract
Studies on event occurrence aim to investigate if
and when subjects experience a particular event. The timing of events may be
measured continuously, using thin precise units, or discretely, using time
periods. The latter metric of time is often used in social science research and
the generalized linear model is an appropriate model for data analysis.
While the design of trials with continuous time survival endpoints has been
extensively studied, hardly any guidelines are available for trials with
discrete time survival endpoints. This presentation will explore the
relationship between sample size and power to detect a treatment effect in a
trial with two treatment conditions. The exponential and Weibull survival
functions will be used to represent constant and varying hazard rates, along
with logit and complementary log-log link functions. It will be seen that for
constant hazard rates the power depends on the event proportions at the end of
the trial in both treatment arms and on the number of time periods. For varying
hazard rates the power also depends on the shape of the survival functions and
different power levels are observed in each time period in the case where the
logit link function is used.
Application of the Delta Method and Poisson Process on Relative Risk by Chihche Lin, Ph.D., Astellas Pharmacueticals
Biographical Background
Dr. Chihche Lin is a senior statistician at Astellas Pharma Global Development.
Chihche is currently working in the Oncology group for late phase studies.
Before joining Astellas in 2008, Chihche received statistics education and
accumulated consulting experience in the School of Statistics, University of
Minnesota, where he was also awarded his Ph.D. degree in 2007. His academia
research interests include machine learning, model selection/combining and
empirical processes. Along with his recent exposure to clinical trial studies,
Chihche's research interests turn to Bayesian Analysis, Survival Analysis and
Adaptive Design.
Abstract
The concept of relative risk has been a prevailing way to describe the
comparison of event rates, especially when the reference event rate is
small. Although many statistical strategies have been approached to realize
relative risk, there are pros and cons, as well as connections and
disconnections among those approaches in practical studies.
This presentation will review several methods for relative risk analysis and
discuss the following topics:
•Confidence intervals of relative risk by using the delta method
•Application of the delta method: An example on constrained confidence
interval for survival rate
•Relative risk adjusted to risk factors.
•Robust analysis of relative risk
•Study design for relative risk analysis with the stopping criterion on the
number of events.
•An application of Poisson processes on the estimation of population size.
Last updated: 02/11/10 by: Clint Lovell
