Modeling recovery curves with application to prostatectomy.

By Fulton Wang, Cynthia Rudin, Tyler H. McCormick, John L. Gore. 2015

6 citations recorded by the source corpus.

In many clinical settings, a patient outcome takes the form of a scalar time series with a recovery curve shape, which is characterized by a sharp drop due to a disruptive event (e.g., surgery) and subsequent monotonic smooth rise towards an asymptotic level not exceeding the pre-event value. 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.

Subfields: Pulmonary and Respiratory Medicine, Statistics and Probability

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In Pulmonary and Respiratory Medicine

Cynthia Rudin

In Statistics and Probability

Citations in the corpus

Stop Explaining Black Box Machine Learning Models for High Stakes Decisions and Use Interpretable Models Instead (2018, background): “[57] create models for recovery curves for prostatectomy patients whose signal and uncertainty obey specific constraints in order to be realistic.”
Please Stop Explaining Black Box Models for High Stakes Decisions (2018, background): “Problems residing in applied statistics are often interpretable because they embed the physics of the domain; e.g., Wang et al. [2018a] create models for recovery curves for prostatectomy patients whose signal and uncertainty obey speciﬁc constraints in order to be realistic.”