title: On inference of control-based imputation for analysis of repeated binary outcomes with missing data.
abstract: Missing data are common in longitudinal clinical trials. How to handle missing data is critical for both sponsors and regulatory agencies to assess treatment effect from the trials. Recently, a control-based imputation has been proposed, where the missing data are imputed based on the assumption that patients who discontinued the test drug will have a similar response profile to the patients in the control group. Under control-based imputation, the variance estimation may be biased using Rubin's formula which could produce biased statistical inferences. We evaluate several statistical methods for obtaining appropriate variances under control-based imputation for analysis of repeated binary outcomes with monotone missing data and show that both the analytical method developed by Robins & Wang and the nonparametric bootstrap method provide more appropriate variance estimates under various simulation settings. We use the methods in an application of an antidepressant Phase III clinical trial and give discussion and recommendations on method performance and preference.
authors.affiliation: a Department of Biostatistics , University of North Carolina , Chapel Hill , North Carolina , USA.
authors.forename: Fei
authors.initials: F
authors.lastname: Gao
authors.affiliation: b Merck & Co., Inc. , Whitehouse Station , New Jersey , USA.
authors.forename: Guanghan
authors.initials: G
authors.lastname: Liu
authors.affiliation: a Department of Biostatistics , University of North Carolina , Chapel Hill , North Carolina , USA.
authors.forename: Donglin
authors.initials: D
authors.lastname: Zeng
authors.affiliation: c Department of Statistics , George Mason University , Fairfax , Virginia , USA.
authors.forename: Guoqing
authors.initials: G
authors.lastname: Diao
authors.affiliation: b Merck & Co., Inc. , Whitehouse Station , New Jersey , USA.
authors.forename: Joseph F
authors.initials: JF
authors.lastname: Heyse
authors.affiliation: a Department of Biostatistics , University of North Carolina , Chapel Hill , North Carolina , USA.|d Department of Biostatistics , University of North Carolina , Chapel Hill , North Carolina , USA.
authors.forename: Joseph G
authors.initials: JG
authors.lastname: Ibrahim
mesh_terms: D000928:Antidepressive Agents / Q000627:therapeutic use; D015982:Bias; D017326:Clinical Trials, Phase III as Topic; D003198:Computer Simulation; D000068598:Data Accuracy; D003627:Data Interpretation, Statistical; D006801:Humans; D008137:Longitudinal Studies
chemical_list: D000928:Antidepressive Agents
keywords: Bootstrap; control-based imputation; missing data; multiple imputation; repeated outcomes