Affiliation:
1. Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
2. MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK
3. NIHR Bristol Biomedical Research Centre, University of Bristol, Bristol, UK
Abstract
Abstract
Background
Missing data are unavoidable in epidemiological research, potentially leading to bias and loss of precision. Multiple imputation (MI) is widely advocated as an improvement over complete case analysis (CCA). However, contrary to widespread belief, CCA is preferable to MI in some situations.
Methods
We provide guidance on choice of analysis when data are incomplete. Using causal diagrams to depict missingness mechanisms, we describe when CCA will not be biased by missing data and compare MI and CCA, with respect to bias and efficiency, in a range of missing data situations. We illustrate selection of an appropriate method in practice.
Results
For most regression models, CCA gives unbiased results when the chance of being a complete case does not depend on the outcome after taking the covariates into consideration, which includes situations where data are missing not at random. Consequently, there are situations in which CCA analyses are unbiased while MI analyses, assuming missing at random (MAR), are biased. By contrast MI, unlike CCA, is valid for all MAR situations and has the potential to use information contained in the incomplete cases and auxiliary variables to reduce bias and/or improve precision. For this reason, MI was preferred over CCA in our real data example.
Conclusions
Choice of method for dealing with missing data is crucial for validity of conclusions, and should be based on careful consideration of the reasons for the missing data, missing data patterns and the availability of auxiliary information.
Funder
Medical Research Council
Alcohol Research UK
National Institute for Health Research
University of Bristol
Publisher
Oxford University Press (OUP)
Subject
General Medicine,Epidemiology
Cited by
385 articles.
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