Addressing bias in prediction models by improving subpopulation calibration.

ABSTRACT:

Objective

To illustrate the problem of subpopulation miscalibration, to adapt an algorithm for recalibration of the predictions, and to validate its performance.

Materials and methods

In this retrospective cohort study, we evaluated the calibration of predictions based on the Pooled Cohort Equations (PCE) and the fracture risk assessment tool (FRAX) in the overall population and in subpopulations defined by the intersection of age, sex, ethnicity, socioeconomic status, and immigration history. We next applied the recalibration algorithm and assessed the change in calibration metrics, including calibration-in-the-large.

Results

1 021 041 patients were included in the PCE population, and 1 116 324 patients were included in the FRAX population. Baseline overall model cal

SUBMITTER: Barda N 

PROVIDER: S-EPMC7936516 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature