|
Activity Number:
|
276
|
|
Type:
|
Contributed
|
|
Date/Time:
|
Tuesday, August 5, 2008 : 10:30 AM to 12:20 PM
|
|
Sponsor:
|
Biopharmaceutical Section
|
| Abstract - #301061 |
|
Title:
|
Resampling-Based Measures for Understanding the Nature of Treatment Effects of Multiple Endpoints in Clinical Trials
|
|
Author(s):
|
Mushfiqur M. Rashid*+ and Mohammad F. Huque
|
|
Companies:
|
U.S. Food and Drug Administration and U.S. Food and Drug Administration
|
|
Address:
|
Division of Biometrics IV/OB/OTS/CDER , Silver Spring, MD 20993, MD, 20993,
|
|
Keywords:
|
Re-sampling Method ; Marginal Model ; Conditional Model ; Multiple Endpoints ; Association Measures ; PTE measure
|
|
Abstract:
|
In a clinical trial, treatment effects of multiple endpoints can be either of overlapping (partially or completely) or of non-overlapping nature. This information is useful in assessing the total benefit of a treatment for a set of multiple endpoints of a trial. An easy-to-understand measure for this purpose is the proportion of the treatment effect (PTE) of a clinical endpoint explained by the treatment effects of other endpoint(s) of interest. However, this ratio estimate has been statistically challenging for some applications as it can produce a wide confidence interval beyond the [0, 1] interval and even the point estimate may fall outside this interval. This article presents a resampling based measure using linear models and a simple resampling based r* measure that avoid the weakness of the conventional PTE measure.
|
