Abstract

Summary 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.

Keywords

ProstatectomyCovariateMonotonic functionBayesian probabilityProstate cancerEvent (particle physics)Computer scienceMedicineEconometricsStatisticsMathematicsCancer

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Publication Info

Year
2018
Type
article
Volume
20
Issue
4
Pages
549-564
Citations
6
Access
Closed

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Fulton Wang, Cynthia Rudin, Tyler H. McCormick et al. (2018). Modeling recovery curves with application to prostatectomy. Biostatistics , 20 (4) , 549-564. https://doi.org/10.1093/biostatistics/kxy002

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DOI
10.1093/biostatistics/kxy002