ABSTRACT Aim To summarize the optimal strategies for dealing with missing binary outcome data (MBOD) in randomized controlled trials (RCTs) as informed by simulation studies, and to summarize the quality of reporting in these studies. Methods To identify simulation studies comparing at least two strategies to deal with MBOD and evaluating their performance (bias, coverage and power), we searched MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials via Ovid, Web of Science, and JSTOR from their inception up to December 20, 2023. We evaluated reporting quality using established criteria for simulation studies in medical statistics. We summarized data using descriptive statistics and a narrative synthesis. Results Our search identified 29,460 citations, of which five proved eligible. Multiple imputation (MI), investigated in five studies, showed consistently good performance in all domains tested for missing completely at random (MCAR) and missing at random (MAR) but with important limitations in missing not at random (MNAR). Complete case analysis (CCA), investigated in four studies of which three addressed model‐based CCA, performed well in bias and coverage under MAR and MCAR, but less well for MNAR. One study reported that non‐model‐based CCA performed poorly with respect to bias under MAR. Non‐model‐based single imputation, investigated in two studies, showed consistently poor performance across all domains tested for MAR, MCAR and MNAR. One study reported that model‐based single imputation performed well with respect to bias under MAR. Regarding reporting quality, all studies reported the aims, dependence of simulated data sets, scenarios and statistical methods evaluated, number of simulations performed, justification of data generation and criteria used to evaluate the simulation performance. None of the studies reported the starting seeds, random number generators and failures occurring during simulation. Conclusions Simulation studies address methods to deal with MBOD in RCTs, provided evidence that the MI approach is superior with respect to bias and coverage compared with CCA. Non‐model‐based single imputation generally performed poorly.
Shen et al. (2025) studied this question.