Covariate Adjustment for Logistic Regression Analysis of Binary Clinical Trial Data. (2nd January 2017)

• Record Type: Journal Article
• Authors: Jiang, Honghua; Kulkarni, Pandurang M.; Mallinckrodt, Craig H.; Shurzinske, Linda; Molenberghs, Geert; Lipkovich, Ilya
• Is Part Of: Statistics in biopharmaceutical research. Volume 9:Number 1(2017)
• Page Start: 126
• Page End: 134
• Publication Date: 2017-01-02
• Subjects: Biased estimates -- Estimands -- Power -- Type I error; Pharmacy -- Statistical methods -- Periodicals; Pharmaceutical biotechnology -- Statistical methods -- Periodicals; Biopharmaceutics -- Periodicals; Biometry -- Periodicals; Pharmacy -- Statistical methods; Periodicals; 615.190727
• DOI: 10.1080/19466315.2016.1234973
• Languages: English
• ISSNs: 1946-6315

Abstract

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 article, 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. Therefore, covariate-adjusted analysis is recommended for evaluating binary outcomes in clinical data.