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Quantile-Optimal Treatment Regimes.

Lan Wang1, Yu Zhou1, Rui Song2

  • 1School of Statistics, University of Minnesota, Minneapolis, MN 55455.

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|November 13, 2018
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Summary
This summary is machine-generated.

This study introduces a robust method for estimating quantile-optimal treatment regimes, crucial for personalized medicine and policy. The research provides new theoretical insights into policy search methods, applicable to both static and dynamic treatment strategies.

Keywords:
dynamic treatment regimenonstandard asymptoticsoptimal treatment regimeprecision medicinequantile criterion

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Area of Science:

  • Statistics
  • Biostatistics
  • Econometrics

Background:

  • Optimal treatment regimes are vital for precision medicine and policy interventions.
  • Current methods typically focus on maximizing average population benefit.
  • Estimating treatment regimes tailored to individual characteristics is a key challenge.

Purpose of the Study:

  • To develop a general framework for estimating the quantile-optimal treatment regime.
  • To provide robust estimation without requiring a pre-specified outcome regression model.
  • To investigate the asymptotic theory of the proposed estimator.

Main Methods:

  • Proposed a novel formulation of the estimator as a solution to an optimization problem.
  • Utilized empirical process techniques to analyze asymptotic theory.
  • Investigated doubly robust estimation and alternative optimality criteria (Gini's mean difference, weighted quantiles).

Main Results:

  • Derived nonstandard convergence rates and non-normal limiting distributions for the estimator.
  • Filled a theoretical gap in policy search methods, applicable to mean optimality as well.
  • Demonstrated estimator performance through numerical simulations and a real-world HIV+ patient data example.

Conclusions:

  • The proposed framework offers a robust approach to estimating quantile-optimal treatment regimes.
  • The theoretical findings advance the understanding of policy search methods.
  • The methods are applicable to static and dynamic treatment regimes and various optimality criteria.