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Is Using Multiple Imputation (MI) Better Than Complete Case (CC) Analysis For Estimating a Prevalence (Risk) Difference In Randomized Controlled Trials When Binary Outcome Observations Are Missing?

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Mukaka, Mavuto, White, Sarah, Terlouw, Anja ORCID: https://orcid.org/0000-0001-5327-8995, Mwapasa, Victor, Kalilani-Phiri, Linda and Faragher, Brian (2016) 'Is Using Multiple Imputation (MI) Better Than Complete Case (CC) Analysis For Estimating a Prevalence (Risk) Difference In Randomized Controlled Trials When Binary Outcome Observations Are Missing?'. Trials, Vol 17, Issue 341.

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Mukaka et al accepted to Trials journal June 2016.pdf - Accepted Version
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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 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.

Methods
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 datasets 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 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.

Item Type: Article
Subjects: W General Medicine. Health Professions > W 20.5 Biomedical research
WC Communicable Diseases > Tropical and Parasitic Diseases > WC 765 Prevention and control
Faculty: Department: Clinical Sciences & International Health > Clinical Sciences Department
Digital Object Identifer (DOI): https://doi.org/10.1186/s13063-016-1473-3
Depositing User: Helen Wong
Date Deposited: 19 Jul 2016 10:17
Last Modified: 13 Sep 2019 13:10
URI: https://archive.lstmed.ac.uk/id/eprint/5969

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