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Multiple Imputation for Incomplete Data in Environmental Epidemiology Research

  • Methods in Environmental Epidemiology (AZ Pollack and NJ Perkins, Section Editors)
  • Published:
Current Environmental Health Reports Aims and scope Submit manuscript

Abstract

Purpose of Review

Incomplete data are a common problem in statistical analysis of environmental epidemiological research. However, many researchers still ignore this complication. We evaluate the performance of two commonly used multiple imputation (MI) methods (fully conditional specification and multivariate normal) for handling missing data and compare them to complete case analysis (CCA) method. We further discuss issues that arise when these methods are being used.

Recent Findings

MI is a simulation-based approach to deal with incomplete data. In general, MI will perform better then ad hoc techniques such as CCA. MI is an approach which replaces the missing data with plausible values and allows for additional uncertainty due to the missing information caused by the incomplete data. To illustrate this, we use data of 944 women from the Collaborative Perinatal Project and compare estimates between these methods. The goal is to examine if each of two outcomes, birth-weight and spontaneous abortion, in the data set are associated with mothers’ smoking status during pregnancy adjusting for baseline covariates in the model.

Summary

Results indicate that MI is better suited for handling incomplete data and led to a significant improvement in parameter estimates compared to CCA. The two MI methods produced similar point estimates, but slightly different standard errors.

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Abbreviations

ANOVA:

analysis of variance

CCA:

complete case analysis

CPP:

Collaborative Perinatal Project

FCS:

fully conditional specification

ICE:

imputation by chained equations

MAR:

missing at random

MCAR:

missing completely at random

MI:

multiple imputation

MICE:

multivariate imputation by chained equations

MCMC:

Markov chain Monte Carlo

MNAR:

missing not at random

MVN:

multivariate normal

SA:

spontaneous abortion

SD:

standard deviation

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Authors and Affiliations

  1. Department of Statistics, College of Liberal Arts and Sciences, University of Connecticut, 215 Glenbrook Rd Unit, Storrs, CT, 4120, USA

    Prince Addo Allotey & Ofer Harel

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  1. Prince Addo Allotey
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  2. Ofer Harel
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Correspondence to Ofer Harel.

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Allotey, P.A., Harel, O. Multiple Imputation for Incomplete Data in Environmental Epidemiology Research. Curr Envir Health Rpt 6, 62–71 (2019). https://doi.org/10.1007/s40572-019-00230-y

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