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Activity Number: 380
Type: Contributed
Date/Time: Tuesday, August 2, 2016 : 10:30 AM to 11:15 AM
Sponsor: Biopharmaceutical Section
Abstract #321594
Title: Covariate Adjustment for Logistic Regression Analysis of Binary Clinical Trial Data
Author(s): Honghua Jiang* and Pandurang Kulkarni and Craig Mallinckrodt and Linda Shurzinske and Geert Molenberghs and Ilya Lipkovich
Companies: and Eli Lilly and Company and Eli Lilly and Company and Eli Lilly and Company and Universiteit Hasselt and Quintiles
Keywords: Biased estimates ; Type I error ; Power ; Estimands
Abstract:

In linear regression models covariate adjusted analysis is not expected to change the estimates of the treatment effect in the clinical trials with randomized treatment assignment but rather to increase the precision of the estimates. However, the covariate adjusted treatment effect estimates are generally not equivalent to the unadjusted estimates in logistic regression analysis for binary clinical trial data. In this paper we report the results of a simulation study conducted to quantify the magnitude of difference between the estimands underlying the two estimators in logistic regression. The simulation results demonstrated that both unadjusted and adjusted analyses preserved type I error at the nominal level. The covariate adjusted analysis produced unbiased, larger treatment effect estimates, larger standard error, and increased power compared with the unadjusted analysis when the sample size was large. The unadjusted analysis resulted in biased estimates of treatment effect. Analysis results for five phase 3 diabetes trials of the same compound were consistent with the simulation findings.


Authors who are presenting talks have a * after their name.

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