Dealing with differential misclassification of an outcome or a covariate in association studies with an internally validated sample selected not at random.

ABSTRACT:

Background

To present an analytical framework for correcting misclassification when an imperfect test is used as an indicator of a disease in association studies, taking into account that part of the sample has joint test and disease data.

Methods

We explored two scenarios, depending on whether the disease is a covariate or the outcome. The analysis sample includes an internal validation sample where the disease status is known in addition to the test. Joint likelihood models taking into account classification errors and the possible selection of the validation sample not at random were used. Simulations were performed to evaluate the methods. We illustrated our framework using data from a multi-cohort COVID-19 serological study conducted in France between 2020 and 2021, wit

SUBMITTER: Osso JL 

PROVIDER: S-EPMC12667108 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature