Bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees. (11th January 2022) - Feng, Jean; Gossmann, Alexej; Sahiner, Berkman; Pirracchio, Romain
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 reviser in terms of the average loss. Results: Both procedures consistently outperformed the static model and other online logistic revision methods. In simulations, the average estimated calibration index (aECI) of the original model was 0.828 (95%CI, 0.818–0.938). Online recalibration using BLR and MarBLR improved the aECI towards the ideal value of zero, attaining 0.265 (95%CI, 0.230–0.300) and 0.241 (95%CI, 0.216–0.266), respectively. When performing more extensive logistic model revisions, BLR and MarBLR increased the average area under the receiver-operating characteristic curve (aAUC) from 0.767 (95%CI, 0.765–0.769) to 0.800 (95%CI, 0.798–0.802) and 0.799 (95%CI, 0.797–0.801), respectively, in stationary settings and protected against substantial model decay. In the COPD study, BLR and MarBLR dynamically combined the original model with a continually refitted gradient boosted tree to achieve aAUCs of 0.924 (95%CI, 0.913–0.935) and 0.925 (95%CI, 0.914–0.935), compared to the static model's aAUC of 0.904 (95%CI, 0.892–0.916). Discussion: Despite its simplicity, BLR is highly competitive with MarBLR. MarBLR outperforms BLR when its prior better reflects the data. Conclusions: BLR and MarBLR can improve the transportability of clinical prediction models and maintain their performance over time.