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Modeling recovery curves with application to prostatectomy.

Fulton Wang1, Cynthia Rudin2, Tyler H Mccormick3

  • 1Department of EECS, Massachusetts Institute of Technology, 77 Massachusetts Ave, Cambridge, MA, USA.

Biostatistics (Oxford, England)
|May 10, 2018
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Summary

This study introduces a Bayesian model to predict patient recovery curves after surgery, aiding medical decisions. The model accurately forecasts sexual function recovery in prostate cancer patients, offering personalized insights.

Keywords:
Bayesian methodsInterpretable modelingProstate cancerRecovery curves

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Oncology

Background:

  • Patient outcomes often present as time series data with a characteristic recovery curve following a disruptive event.
  • Prostate cancer surgery (prostatectomy) significantly impacts patient recovery, particularly sexual function.

Purpose of the Study:

  • To develop a Bayesian model for predicting patient recovery curves using pre-event data.
  • To apply the model to forecast sexual function recovery in prostate cancer patients post-prostatectomy.
  • To serve as a pre-treatment decision aid for personalized medical care.

Main Methods:

  • A Bayesian statistical modeling approach was employed.
  • The model predicts scalar time series with a recovery curve shape.
  • Information available before the disruptive event (surgery) was utilized for predictions.

Main Results:

  • The model accurately predicts recovery curves, specifically sexual function post-prostatectomy.
  • Personalized predictions were generated, enhancing interpretability and accuracy.
  • Identified covariate relationships align with and expand upon existing medical literature.

Conclusions:

  • The proposed Bayesian model is a valuable tool for predicting patient recovery trajectories.
  • The model functions effectively as a pre-treatment medical decision aid, offering personalized insights.
  • Findings contribute to understanding recovery patterns and covariate influences in prostate cancer patients.