American Statistical Association
New York City
Metropolitan Area Chapter

New York State Psychiatric Institute
at Columbia University Medical Center
Biostatistics Seminar

Co-Sponsor: Department of Biostatistics,
Mailman School of Public Health, Columbia University



CAUSAL VS. STANDARD APPROACHES TO STRATIFYING
ON POST-RANDOMIZATION FACTORS IN CLINICAL TRIALS

by

Thomas Ten Have, Ph.D.
Professor of Biostatistics
Department of Biostatistics
University of Pennsylvania


Abstract

We address several questions relating to the use of standard regression and causal approaches to analyzing how post-randomization factors may modify the intent-to-treat effects of randomized interventions. In assessing these questions, we present a causal linear rank preserving model (RPM) for analyzing the modification of a randomized baseline intervention's effect on a single endpoint outcome by post-randomization factors. Unlike standard interaction analyses in such a context, our approach does not assume that the post-randomization effect modifier is also randomly assigned to individuals in addition to the randomized baseline intervention (i.e., sequential ignorability). Our simulation results suggest that even without the sequential ignorability assumption, the standard regression method performs as well as the causal approach in terms of inference for the interaction term. Furthermore, additional conditions are needed for the standard regression approach to perform more poorly than the causal approach in estimating ITT effects stratified by the post randomization factor. An important condition may be a strong ITT effect on the post-randomization factor. These issues and methods are illustrated with application of the standard and causal methods to a randomized cognitive behavioral therapy (CBT) trial example. The behavioral theory of a common treatment effect motivates the focus on interactions between CBT and post-randomization behavioral factors such as negative problem solving and suicide ideation with depression as the outcome.

Biographical Note

Thomas Ten Have, Professor of Biostatistics, has statistical research interests in categorical data analysis, random effects models, informative dropout, causal models, mediation and moderation analyses, treatment non-adherence, and designs and statistical analyses to accommodate patient preferences and adaptive treatment regimes. This methods research melds with his collaborations in psychiatry, family medicine, addiction research, and disparities research, with a focus on multi-site randomized and observational studies. Dr. Ten Have’s research has been facilitated by his roles as the Principal Investigator of NIH-funded R01 and training grants and the Co-Investigator of a number of complex intervention studies requiring development of new methods for analyzing such studies. Finally, Dr. Ten Have is strongly committed to affirmative action in the recruitment of students, faculty members, investigators, study participants, and research topics.


Date: Tuesday, April 28, 2009
Time: 3:00 - 4:00 P.M.
Location: New York State Psychiatric Institute
1051 Riverside Drive
6th Floor Multi-Purpose Room (6602)
New York, New York
(Directions)

RESERVATIONS ARE NOT REQUIRED

Refreshments will be served from 2:45 to 3:00 P.M.,
with a reception from 4:00 to 4:30 P.M.


Home Page | Chapter News | Chapter Officers | Chapter Events
Other Metro Area Events | ASA National Home Page | Links To Other Websites
NYC ASA Chapter Constitution | NYC ASA Chapter By-Laws

Copyright © 2008-2009 by New York City Metropolitan Area Chapter of the ASA
Designed and maintained by Cynthia Scherer
Send questions or comments to nycasa@mindspring.com