A nonparametric updating method to correct clinical prediction model drift.

ABSTRACT:

Objective

Clinical prediction models require updating as performance deteriorates over time. We developed a testing procedure to select updating methods that minimizes overfitting, incorporates uncertainty associated with updating sample sizes, and is applicable to both parametric and nonparametric models.

Materials and methods

We describe a procedure to select an updating method for dichotomous outcome models by balancing simplicity against accuracy. We illustrate the test's properties on simulated scenarios of population shift and 2 models based on Department of Veterans Affairs inpatient admissions.

Results

In simulations, the test generally recommended no update under no population shift, no update or modest recalibration under case mix shifts, intercept correctio

SUBMITTER: Davis SE 

PROVIDER: S-EPMC6857513 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature