Modeling recovery curves with application to prostatectomy.
Journal Article (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
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