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.
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.
authors: Eli Lilly and Company, Lilly Corporate Center, Indianapolis, IN, USA.
authors: Honghua
authors: H
authors: Jiang
authors: Eli Lilly and Company, Global Statistical Science, Indianapolis, IN, USA.
authors: Pandurang M
authors: PM
authors: Kulkarni
authors: Lilly, Lilly Corporate Center, Indianapolis, IN, USA.
authors: Craig H
authors: CH
authors: Mallinckrodt
authors: Eli Lilly and Company, Global Statistical Science, Indianapolis, IN, USA.
authors: Linda
authors: L
authors: Shurzinske
authors: I-BioStat, Universiteit Hasselt, Diepenbeek, Belgium, & Katholieke Universiteit Leuven, Belgium.
authors: Geert
authors: G
authors: Molenberghs
authors: Eli Lilly, Statistics, Indiana, IN, USA.
authors: Ilya
authors: I
authors: Lipkovich
mesh_terms: D015982:Bias; D016009:Chi-Square Distribution; D017326:Clinical Trials, Phase III as Topic / Q000379:methods / Q000592:standards; D003627:Data Interpretation, Statistical; D003920:Diabetes Mellitus / Q000188:drug therapy; D006442:Glycated Hemoglobin / Q000032:analysis; D006801:Humans; D007004:Hypoglycemic Agents / Q000627:therapeutic use; D016014:Linear Models; D016015:Logistic Models; D016032:Randomized Controlled Trials as Topic / Q000379:methods* / Q000592:standards; D016896:Treatment Outcome
chemical_list: D006442:Glycated Hemoglobin; D007004:Hypoglycemic Agents; C517652:hemoglobin A1c protein, human
keywords: power; repeated binary response; type I error