Accounting for missing data in statistical analyses: multiple imputation is not always the answer.

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

SUBMITTER: Hughes RA 

PROVIDER: S-EPMC6693809 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature