Observability and its impact on differential bias for clinical prediction models.

ABSTRACT:

Objective

Electronic health records have incomplete capture of patient outcomes. We consider the case when observability is differential across a predictor. Including such a predictor (sensitive variable) can lead to algorithmic bias, potentially exacerbating health inequities.

Materials and methods

We define bias for a clinical prediction model (CPM) as the difference between the true and estimated risk, and differential bias as bias that differs across a sensitive variable. We illustrate the genesis of differential bias via a 2-stage process, where conditional on having the outcome of interest, the outcome is differentially observed. We use simulations and a real-data example to demonstrate the possible impact of including a sensitive variable in a CPM.

Results

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SUBMITTER: Yan M 

PROVIDER: S-EPMC9006687 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature