Dealing with missing covariates in epidemiologic studies: a comparison between multiple imputation and a full Bayesian approach

Volume: 35, Issue: 17, Pages: 2955 - 2974
Published: Apr 4, 2016
Abstract
Incomplete data are generally a challenge to the analysis of most large studies. The current gold standard to account for missing data is multiple imputation, and more specifically multiple imputation with chained equations (MICE). Numerous studies have been conducted to illustrate the performance of MICE for missing covariate data. The results show that the method works well in various situations. However, less is known about its performance in...
Paper Details
Title
Dealing with missing covariates in epidemiologic studies: a comparison between multiple imputation and a full Bayesian approach
Published Date
Apr 4, 2016
Volume
35
Issue
17
Pages
2955 - 2974
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