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?

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?

© 2016 Mukaka et al. 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 whi...

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Bibliographic Details
Main Authors: Mavuto Mukaka, Sarah A. White, Dianne J. Terlouw, Victor Mwapasa, Linda Kalilani-Phiri, E. Brian Faragher
Other Authors: University of Malawi College of Medicine
Format: Article
Published: 2018
Subjects:
Medicine
Online Access:https://repository.li.mahidol.ac.th/handle/123456789/41263
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Institution: Mahidol University

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https://repository.li.mahidol.ac.th/handle/123456789/41263

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