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:

Mukaka Mavuto,White Sarah A.,Terlouw Dianne J.,Mwapasa Victor,Kalilani-Phiri Linda,Faragher E. Brian

Funder

European and Developing Countries Clinical Trials Partnership (NL)

Johns Hopkins University Center for AIDS Research

Publisher

Springer Science and Business Media LLC

Subject

Pharmacology (medical),Medicine (miscellaneous)

Reference24 articles.

1. Montori VM, Guyatt GH. Intention-to-treat principle. CMAJ. 2001;165:1339–41.

2. Machekano RN, Dorsey G, Hubbard A. Efficacy studies of malaria treatments in Africa: efficient estimation with missing indicators of failure. Stat Methods Med Res. 2008;17:191–206.

3. Higgins JP, White IR, Wood AM. Imputation methods for missing outcome data in meta-analysis of clinical trials. Clin Trials. 2008;5:225–39.

4. Wood AM, White IR, Thompson SG. Are missing outcome data adequately handled? A review of published randomized controlled trials in major medical journals. Clin Trials. 2004;1:368–76.

5. Panel on Handling Missing Data in Clinical Trials, Committee on National Statistics Division of Behavioral and Social Sciences and Education, National Research Council. The prevention and treatment of missing data in clinical trials. Washington, DC: The National Academies Press; 2010.

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