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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/18958

Title: To adjust or not to adjust for baseline when analyzing repeated binary responses? The case of complete data when treatment comparison at study end is of interest
Authors: Jiang, Honghua
Kulkarni, Pandurang M.
Mallinckrodt, Craig H.
Shurzinske, Linda
Molenberghs, Geert
Lipkovich, Ilya
Issue Date: 2015
Citation: PHARMACEUTICAL STATISTICS, 14 (3), p. 262-271
Abstract: The benefits of adjusting for baseline covariates are not as straightforward with repeated binary responses as with continuous response variables. Therefore, in this study, we compared different methods for analyzing repeated binary data through simulations when the outcome at the study endpoint is of interest. Methods compared included chi-square, Fisher's exact test, covariate adjusted/unadjusted logistic regression (Adj.logit/Unadj.logit), covariate adjusted/unadjusted generalized estimating equations (Adj.GEE/Unadj.GEE), covariate adjusted/unadjusted generalized linear mixed model (Adj.GLMM/Unadj.GLMM). All these methods preserved the type I error close to the nominal level. Covariate adjusted methods improved power compared with the unadjusted methods because of the increased treatment effect estimates, especially when the correlation between the baseline and outcome was strong, even though there was an apparent increase in standard errors. Results of the Chi-squared test were identical to those for the unadjusted logistic regression. Fisher's exact test was the most conservative test regarding the type I error rate and also with the lowest power. Without missing data, there was no gain in using a repeated measures approach over a simple logistic regression at the final time point. Analysis of results from five phase III diabetes trials of the same compound was consistent with the simulation findings. Therefore, covariate adjusted analysis is recommended for repeated binary data when the study endpoint is of interest. Copyright (c) 2015John Wiley & Sons, Ltd.
Notes: [Jiang, Honghua] Eli Lilly & Co, Lilly Corp Ctr, Indianapolis, IN 46285 USA. [Kulkarni, Pandurang M.; Shurzinske, Linda] Eli Lilly & Co, Global Stat Sci, Indianapolis, IN 46285 USA. [Mallinckrodt, Craig H.] Lilly, Lilly Corp Ctr, Indianapolis, IN USA. [Molenberghs, Geert] Univ Hasselt, I BioStat, Diepenbeek, Belgium. [Molenberghs, Geert] Katholieke Univ Leuven, Leuven, Belgium. [Lipkovich, Ilya] Eli Lilly, Stat, Indianapolis, IN USA. Correspondence to: Honghua Jiang, Eli Lilly and Company, Lilly Corporate Center, Indianapolis, Indiana, United States, 46285. E-mail: jianghh@lilly.com
URI: http://hdl.handle.net/1942/18958
DOI: 10.1002/pst.1682
ISI #: 000354417000012
ISSN: 1539-1604
Category: A1
Type: Journal Contribution
Validation: ecoom, 2016
Appears in Collections: Research publications

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