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Modeling recovery curves with application to prostatectomy.

Authors
  • Wang, Fulton1
  • Rudin, Cynthia2
  • Mccormick, Tyler H3
  • Gore, John L4
  • 1 Department of EECS, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, USA.
  • 2 Department of Computer Science, Duke University, LSRC D342, Research Drive, Durham, NC, USA.
  • 3 Department of Statistics and Department of Sociology, University of Washington, Seattle, WA, USA.
  • 4 Department of Urology, University of Washington, 959 NE Pacific St, Seattle, WA, USA.
Type
Published Article
Journal
Biostatistics (Oxford, England)
Publication Date
Oct 01, 2019
Volume
20
Issue
4
Pages
549–564
Identifiers
DOI: 10.1093/biostatistics/kxy002
PMID: 29741607
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

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. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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