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?

• Main Authors: Mukaka, Mavuto, White, Sarah A., Terlouw, Dianne J., Victor Mwapasa, Linda Kalilani-Phiri, Faragher, E. Brian
• Other Authors: Mahidol University. Faculty of Tropical Medicine. Mahidol-Oxford Tropical Medicine Research Unit
• Format: Article
• Language: English
• Published: 2017
• Subjects: Open Access article; Missing binary outcome; Risk difference; Complete case analysis; Multiple imputation; Missing completely at random; Missing at random; Missing not at random
• Online Access: https://repository.li.mahidol.ac.th/handle/123456789/3154
• Institution: Mahidol University

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 p...