|
Activity Number:
|
444
|
|
Type:
|
Invited
|
|
Date/Time:
|
Wednesday, August 1, 2007 : 2:00 PM to 3:50 PM
|
|
Sponsor:
|
ENAR
|
| Abstract - #308084 |
|
Title:
|
Causal Comparisons in Randomized Trials of Two Active Treatments
|
|
Author(s):
|
Jason Roy and Joseph W. Hogan*+
|
|
Companies:
|
Geisinger Health System and Brown University
|
|
Address:
|
Box G-S121, Providence, RI, 02912,
|
|
Keywords:
|
noncompliance ; smoking cessation ; potential outcomes ; bounds ; principal stratification ; Bayesian inference
|
|
Abstract:
|
In behavioral medicine trials, such as smoking cessation trials, two or more active treatments are often compared. Causal parameters of interest might include those defined by subpopulations based on their potential compliance status under each assignment. Principal stratification is an effective method for inference, but even when subjects in one arm do not have access to the other treatment, the causal effect of each treatment typically can only be identified within certain bounds. We propose to use additional information---compliance-predictive covariates---to improve identification of the causal effects. We demonstrate the benefit of utilizing covariate information in both a simulation study and in an analysis of data from a smoking cessation trial.
|
