Modeling Recovery Curves With Application to Prostatectomy

In the medical community, there is a pressing need for personalized predictions of how a disruptive event, such as a treatment or disease, will impact particular bodily function levels. Of particular interest is the extent to which the function is initially perturbed by the event and the ensuing pattern of recovery. In many contexts, such as mental acuity following a stroke or sexual function following prostatectomy, the post-event trajectory generally exhibits what we call a recovery curve shape, characterized by an initial instantaneous drop followed by a monotonic rise towards an asymptotic level not exceeding the original function level. Here, we propose a Bayesian model that can be used to predict a patient's expected recovery curve, given information about the patient that is available before the event. This paper presents a decision aid for patients considering a medical treatment who want to know what adverse side effect the treatment would have on a particular bodily function. In particular, our model will be used to display to the patient a distribution over post-treatment function trajectories, conveying the uncertainty in predictions that should be considered in decision-making.