A Comparison of Methods to Detect Changes in Prediction Models.

ABSTRACT:

Background

Prediction models inform decisions in many areas of medicine. Most models are fitted once and then applied to new (future) patients, despite the fact that model coefficients can vary over time due to changes in patients' clinical characteristics and disease risk. However, the optimal method to detect changes in model parameters has not been rigorously assessed.

Methods

We simulated data, informed by post-lung transplant mortality data and tested the following two approaches for detecting model change: (1) the "Direct Approach," it compares coefficients of the model refit on recent data to those at baseline; and (2) "Calibration Regression," it fits a logistic regression model of the log-odds of the observed outcomes versus the linear predictor from the baseline mo

SUBMITTER: Schnellinger EM 

PROVIDER: S-EPMC10413959 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature