Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees.

ABSTRACT:

Objective

After deploying a clinical prediction model, subsequently collected data can be used to fine-tune its predictions and adapt to temporal shifts. Because model updating carries risks of over-updating/fitting, we study online methods with performance guarantees.

Materials and methods

We introduce 2 procedures for continual recalibration or revision of an underlying prediction model: Bayesian logistic regression (BLR) and a Markov variant that explicitly models distribution shifts (MarBLR). We perform empirical evaluation via simulations and a real-world study predicting Chronic Obstructive Pulmonary Disease (COPD) risk. We derive "Type I and II" regret bounds, which guarantee the procedures are noninferior to a static model and competitive with an oracle logistic revi

SUBMITTER: Feng J 

PROVIDER: S-EPMC9006691 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature