Journal
AMERICAN JOURNAL OF EPIDEMIOLOGY
Volume 171, Issue 5, Pages 624-632Publisher
OXFORD UNIV PRESS INC
DOI: 10.1093/aje/kwp425
Keywords
data interpretation; statistical; epidemiologic methods; imputation; incomplete data; missing data; simulations
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Funding
- Australian National Health and Medical Research Council [334336]
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Statistical analysis in epidemiologic studies is often hindered by missing data, and multiple imputation is increasingly being used to handle this problem. In a simulation study, the authors compared 2 methods for imputation that are widely available in standard software: fully conditional specification (FCS) or chained equations and multivariate normal imputation (MVNI). The authors created data sets of 1,000 observations to simulate a cohort study, and missing data were induced under 3 missing-data mechanisms. Imputations were performed using FCS (Royston's ice) and MVNI (Schafer's NORM) in Stata (Stata Corporation, College Station, Texas), with transformations or prediction matching being used to manage nonnormality in the continuous variables. Inferences for a set of regression parameters were compared between these approaches and a complete-case analysis. As expected, both FCS and MVNI were generally less biased than complete-case analysis, and both produced similar results despite the presence of binary and ordinal variables that clearly did not follow a normal distribution. Ignoring skewness in a continuous covariate led to large biases and poor coverage for the corresponding regression parameter under both approaches, although inferences for other parameters were largely unaffected. These results provide reassurance that similar results can be expected from FCS and MVNI in a standard regression analysis involving variously scaled variables.
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