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Trials. 2016 Jul 22;17:341. doi: 10.1186/s13063-016-1473-3.

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?

Author information

1
Malawi-Liverpool-Wellcome Trust Clinical Research Programme, College of Medicine, University of Malawi, Box 30096, Blantyre 3, Malawi. mmukaka@gmail.com.
2
Department of Public Health, College of Medicine, University of Malawi, P/Bag 360, Blantyre 3, Malawi. mmukaka@gmail.com.
3
Liverpool School of Tropical Medicine, Pembroke Place, L3 5QA, Liverpool, UK. mmukaka@gmail.com.
4
Mahidol-Oxford Tropical Medicine Research Unit, Mahidol University, 60th Anniversary Chalermprakiat Building, 3rd Floor, 420/6 Ratchawithi Rd, Bangkok, 10400, Thailand. mmukaka@gmail.com.
5
Department of Public Health, College of Medicine, University of Malawi, P/Bag 360, Blantyre 3, Malawi.
6
Liverpool School of Tropical Medicine, Pembroke Place, L3 5QA, Liverpool, UK.
7
Malawi-Liverpool-Wellcome Trust Clinical Research Programme, College of Medicine, University of Malawi, Box 30096, Blantyre 3, Malawi.

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.

KEYWORDS:

Complete case analysis; Missing at random; Missing binary outcome; Missing completely at random; Missing not at random; Multiple imputation; Risk difference

PMID:
27450066
PMCID:
PMC4957845
DOI:
10.1186/s13063-016-1473-3
[Indexed for MEDLINE]
Free PMC Article

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