Modeling Recovery Curves With Application to Prostatectomy
This work addresses the need for pre-treatment decision aids in medical settings, offering incremental improvements by supplementing existing literature with new covariate insights.
The authors tackled the problem of predicting personalized recovery curves for prostate cancer patients after prostatectomy surgery, using a Bayesian model that provides interpretable and accurate predictions based on pre-treatment information.
We propose a Bayesian model that predicts recovery curves based on information available before the disruptive event. A recovery curve of interest is the quantified sexual function of prostate cancer patients after prostatectomy surgery. We illustrate the utility of our model as a pre-treatment medical decision aid, producing personalized predictions that are both interpretable and accurate. We uncover covariate relationships that agree with and supplement that in existing medical literature.