Inverse probability weighted Cox regression to correct for ascertainment bias

Inverse probability weighted Cox regression to correct for ascertainment bias


2022-09-29 - 14:30

Mar Rodríguez-Girondo

Department of Biomedical Data Sciences - Leiden University Medical Center

Motivated by the study of genetic effect modifiers of cancer, we examine weighting approaches to
correct for ascertainment bias of covariate effects in the context of Cox proportional hazards
regression. (Family-based) outcome-dependent sampling is common in genetic epidemiology leading
to study samples with too many events in comparison to the population and an overrepresentation
of young, affected subjects. A usual approach for correcting for ascertainment bias in this setting is
to use an inverse probability weighted Cox model, using weights based on external available
population-based age-specific incidence rates of the type of cancer under investigation. However,
the current approach relies on the assumption of oversampling of cases of all ages which is not
realistic in relevant practical settings. Based on the same principle of weighting observations by
their inverse probability of selection, we propose a new, more general approach. We compare the
methods in simulations and illustrate the advantage of our new method with two real datasets. In
both applications, the goal is to assess the association between common susceptibility loci identified
in Genome-Wide Association Studies (GWAS) and cancer (colorectal and breast) using data collected
through genetic testing in clinical genetics centers of the Netherlands.

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