Evaluation of multiple imputation approaches for handling missing covariate information in a case-cohort study with a binary outcome.

ABSTRACT:

Background

In case-cohort studies a random subcohort is selected from the inception cohort and acts as the sample of controls for several outcome investigations. Analysis is conducted using only the cases and the subcohort, with inverse probability weighting (IPW) used to account for the unequal sampling probabilities resulting from the study design. Like all epidemiological studies, case-cohort studies are susceptible to missing data. Multiple imputation (MI) has become increasingly popular for addressing missing data in epidemiological studies. It is currently unclear how best to incorporate the weights from a case-cohort analysis in MI procedures used to address missing covariate data.

Method

A simulation study was conducted with missingness in two covariates, motivated b

SUBMITTER: Middleton M 

PROVIDER: S-EPMC8978363 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature