Modeling Recovery Curves With Application to Prostatectomy

Abstract

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.

Accepted to Biostatistics, 2018. Includes supplementary material and high resolution images of predictions for patients