Multiple Imputation for Missing Data: Fully Conditional Specification Versus Multivariate Normal Imputation

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ID: 307237
2010
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Abstract
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.
Reference Key
openalex_W2143721482 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors K. J. Lee, John B. Carlin
Journal american journal of epidemiology
Year 2010
DOI
10.1093/aje/kwp425
URL
https://academic.oup.com/aje/article-pdf/171/5/624/318860/kwp425.pdf
Keywords Keywords not found

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