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http://hdl.handle.net/1942/24157
Title: | Covariate Adjustment for Logistic Regression Analysis of Binary Clinical Trial Data | Authors: | Jiang, Honghua Kulkarni, Pandurang M. Mallinckrodt, Craig H. Shurzinske, Linda MOLENBERGHS, Geert Lipkovich, Ilya |
Issue Date: | 2017 | Publisher: | AMER STATISTICAL ASSOC | Source: | STATISTICS IN BIOPHARMACEUTICAL RESEARCH, 9(1), p. 126-134 | 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 comparedwith the unadjusted analysiswhen the sample sizewas 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. | Notes: | [Jiang, Honghua; Kulkarni, Pandurang M.; Mallinckrodt, Craig H.; Shurzinske, Linda] Eli Lilly & Co, Lilly Res Labs, Indianapolis, IN 46285 USA. [Molenberghs, Geert] Hasselt Univ, I BioStat, Diepenbeek, Belgium. [Molenberghs, Geert] Katholieke Univ Leuven, I BioStat, Leuven, Belgium. [Lipkovich, Ilya] Quintiles, Morrisville, NC USA. | Keywords: | Biased estimates; Estimands; Power; Type I error;biased estimates; estimands; power; type I error | Document URI: | http://hdl.handle.net/1942/24157 | ISSN: | 1946-6315 | e-ISSN: | 1946-6315 | DOI: | 10.1080/19466315.2016.1234973 | ISI #: | 000397258400013 | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2018 |
Appears in Collections: | Research publications |
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jiang2016.pdf | Peer-reviewed author version | 786.3 kB | Adobe PDF | View/Open |
Covariate.pdf Restricted Access | Published version | 729.85 kB | Adobe PDF | View/Open Request a copy |
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