Modeling recovery curves with application to prostatectomy.

Published

Journal Article

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.

Full Text

Duke Authors

Cited Authors

  • Wang, F; Rudin, C; Mccormick, TH; Gore, JL

Published Date

  • October 2019

Published In

Volume / Issue

  • 20 / 4

Start / End Page

  • 549 - 564

PubMed ID

  • 29741607

Pubmed Central ID

  • 29741607

Electronic International Standard Serial Number (EISSN)

  • 1468-4357

International Standard Serial Number (ISSN)

  • 1465-4644

Digital Object Identifier (DOI)

  • 10.1093/biostatistics/kxy002

Language

  • eng