TY - JOUR
T1 - A Systematic Survey of the Optimal Strategy for Dealing With Missing Binary Outcomes in Simulation Studies of Randomized Controlled Trials
AU - Shen, Yanjiao
AU - Sameer, Parpia
AU - Xia, Xin
AU - Zhang, Yuqing
AU - Ma, Jinhui
AU - Shi, Qingyang
AU - Hao, Qiukui
AU - Gu, Xianlin
AU - He, Wenbo
AU - Chen, Yamin
AU - Zhang, Na
AU - Wang, Le
AU - Zeng, Yating
AU - Su, Xiaoyi
AU - Zong, Qiang
AU - Zhi, Qiao
AU - Liu, Sitong
AU - Wang, Xinyao
AU - Zou, Xinyu
AU - He, Ying
AU - Guo, Qiong
AU - Wang, Borong
AU - Du, Liang
AU - Li, Zhengchi
AU - Huang, Jin
AU - Gordon, Guyatt
N1 - Publisher Copyright:
© 2025 Chinese Cochrane Center, West China Hospital of Sichuan University and John Wiley & Sons Australia, Ltd.
PY - 2025/9
Y1 - 2025/9
N2 - 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.
AB - 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.
KW - binary missing outcome data
KW - imputation strategy
KW - randomized control trial
KW - reporting quality
KW - simulation study
UR - https://www.scopus.com/pages/publications/105014122113
U2 - 10.1111/jebm.70058
DO - 10.1111/jebm.70058
M3 - Article
C2 - 40856174
AN - SCOPUS:105014122113
SN - 1756-5383
VL - 18
JO - Journal of Evidence-Based Medicine
JF - Journal of Evidence-Based Medicine
IS - 3
M1 - e70058
ER -