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Handling missing data in matched case-control studies using multiple imputation.

Shaun R Seaman¹, Ruth H Keogh²

¹MRC Biostatistics Unit, Cambridge, CB2 0SR, U.K.

|August 4, 2015

Summary

This summary is machine-generated.

Multiple imputation (MI) offers an efficient solution for missing covariate data in matched case-control studies. Including matching variables in the imputation model improves efficiency and reduces bias in odds ratio estimates.

Keywords:

Chained equations Compatibility MICE Multilevel MI Restricted general location model

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Area of Science:

Biostatistics
Epidemiology
Statistical Modeling

Background:

Missing covariate data complicates matched case-control study analysis.
Complete case analysis is inefficient and potentially biased.
Multiple imputation (MI) presents a flexible and efficient alternative.

Purpose of the Study:

To evaluate and compare different multiple imputation (MI) approaches for matched case-control studies with missing covariate data.
To assess the efficiency and bias of various imputation methods, including full-conditional specification (FCS) and joint modeling (normal and latent normal).

Main Methods:

Two MI approaches were investigated: modeling individual data including matching variables, and modeling entire matched sets.
Within each approach, three methods were compared: FCS, joint normal model MI, and joint latent normal model MI.
Methods were evaluated using a simulation study and illustrated with colorectal cancer data from the EPIC-Norfolk study.

Main Results:

The MI approach incorporating matching variables demonstrated superior efficiency.
FCS MI generally provided the least biased odds ratio estimates.
Joint normal or latent normal MI offered greater efficiency in some scenarios, with all methods showing good confidence interval coverage.

Conclusions:

Multiple imputation methods effectively handle missing covariate data in matched case-control studies.
The choice of MI approach and method impacts efficiency and bias; incorporating matching variables is recommended for efficiency.
FCS MI is recommended for minimizing bias, while joint models may offer efficiency gains, all while ensuring valid statistical inference.