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Utility-based Bayesian personalized treatment selection for advanced breast cancer.

Juhee Lee1, Peter F Thall2, Bora Lim3

  • 1Department of Statistics, University of California Santa Cruz, Santa Cruz, CA.

Journal of the Royal Statistical Society. Series C, Applied Statistics
|January 30, 2023
PubMed
Summary

A new Bayesian method aids personalized treatment selection using a patient-specific utility function. This approach optimizes risk-benefit trade-offs, recommending different breast cancer therapies based on patient age.

Keywords:
Bayesian nonparametricsDependent Dirichlet processMultivariate probit regressionPrecision medicineStatistical decision makingUtility function

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Area of Science:

  • Biostatistics
  • Oncology
  • Personalized Medicine

Background:

  • Randomized clinical trials often yield multiple outcomes, complicating treatment selection.
  • Hormone receptor-positive advanced breast cancer treatment involves balancing efficacy and toxicity.
  • Patient age influences tolerance to severe toxicities, necessitating individualized treatment strategies.

Purpose of the Study:

  • To propose a Bayesian method for personalized treatment selection in trials with multiple outcomes.
  • To develop a framework for quantifying risk-benefit trade-offs using age-dependent utility functions.
  • To apply the method to a breast cancer trial comparing combination therapy to monotherapy.

Main Methods:

  • Elicitation of a utility function from oncologists, varying with patient age.
  • Fitting a Bayesian nonparametric multivariate regression model with a dependent Dirichlet process prior.
  • Utilizing posterior predictive utility distributions for treatment selection.

Main Results:

  • The developed Bayesian method enables personalized treatment recommendations.
  • A utility function incorporating age effectively quantifies risk-benefit trade-offs.
  • For advanced breast cancer, combination therapy (letrozole plus bevacizumab) is preferred for patients ≤70 years, while monotherapy (letrozole alone) is preferred for patients >70 years.

Conclusions:

  • Personalized treatment selection can be optimized by integrating patient-specific utility functions into Bayesian models.
  • The proposed method provides a data-driven approach to tailor therapies based on individual risk-benefit profiles.
  • Age is a critical factor in determining optimal treatment strategies for advanced breast cancer, balancing progression-free survival and toxicity.