On the use of multiple imputation to address data missing by design as well as unintended missing data in case-cohort studies with a binary endpoint.

ABSTRACT:

Background

Case-cohort studies are conducted within cohort studies, with the defining feature that collection of exposure data is limited to a subset of the cohort, leading to a large proportion of missing data by design. Standard analysis uses inverse probability weighting (IPW) to address this intended missing data, but little research has been conducted into how best to perform analysis when there is also unintended missingness. Multiple imputation (MI) has become a default standard for handling unintended missingness and is typically used in combination with IPW to handle the intended missingness due to the case-control sampling. Alternatively, MI could be used to handle both the intended and unintended missingness. While the performance of an MI-only approach has been investig

SUBMITTER: Middleton M 

PROVIDER: S-EPMC10702035 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature