Publication: Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?
Issued Date
2016
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eng
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Mahidol University
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BioMed Central
Bibliographic Citation
Trials. Vol.17, (2016), 341
Suggested Citation
Mukaka, Mavuto, White, Sarah A., Terlouw, Dianne J., Victor Mwapasa, Linda Kalilani-Phiri, Faragher, E. Brian Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?. Trials. Vol.17, (2016), 341. doi:10.1186/s13063-016-1473-3 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/3154
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Title
Is using multiple imputation better than complete case analysis for estimating a prevalence (risk) difference in randomized controlled trials when binary outcome observations are missing?
Abstract
Background: Missing outcomes can seriously impair the ability to make correct inferences from randomized
controlled trials (RCTs). Complete case (CC) analysis is commonly used, but it reduces sample size and is perceived
to lead to reduced statistical efficiency of estimates while increasing the potential for bias. As multiple imputation
(MI) methods preserve sample size, they are generally viewed as the preferred analytical approach.
We examined this assumption, comparing the performance of CC and MI methods to determine risk difference (RD)
estimates in the presence of missing binary outcomes. We conducted simulation studies of 5000 simulated data
sets with 50 imputations of RCTs with one primary follow-up endpoint at different underlying levels of RD (3–25 %)
and missing outcomes (5–30 %).
Results: For missing at random (MAR) or missing completely at random (MCAR) outcomes, CC method estimates
generally remained unbiased and achieved precision similar to or better than MI methods, and high statistical
coverage. Missing not at random (MNAR) scenarios yielded invalid inferences with both methods. Effect size
estimate bias was reduced in MI methods by always including group membership even if this was unrelated to
missingness. Surprisingly, under MAR and MCAR conditions in the assessed scenarios, MI offered no statistical
advantage over CC methods.
Conclusion: While MI must inherently accompany CC methods for intention-to-treat analyses, these findings
endorse CC methods for per protocol risk difference analyses in these conditions. These findings provide an
argument for the use of the CC approach to always complement MI analyses, with the usual caveat that the validity
of the mechanism for missingness be thoroughly discussed. More importantly, researchers should strive to collect
as much data as possible.