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Multi-Response Based Personalized Treatment Selection with Data from Crossover Designs for Multiple Treatments.

K B Kulasekera1, Chathura Siriwardhana2

  • 1Department of Bioinformatics & Biostatistics, University of Louisville, Louisville, KY 40202, USA.

Communications in Statistics: Simulation and Computation
|March 18, 2022
PubMed
Summary

This study introduces a new method for selecting treatments using individual data in crossover trials with multiple outcomes. The approach ranks treatments effectively, improving personalized medicine strategies.

Keywords:
Crossover DesignsDesign variablesMultiple ResponsesPersonalized TreatmentsSingle Index Models

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Personalized Medicine

Background:

  • Treatment selection is challenging with multivariate outcomes and complex trial designs.
  • Existing methods may not fully leverage individual covariate information in crossover studies.

Purpose of the Study:

  • To propose a novel method for treatment selection using individual covariate data.
  • To accommodate multivariate treatment responses and crossover designs.
  • To develop a flexible approach applicable to various statistical models and any number of treatments.

Main Methods:

  • Utilizes rank aggregation techniques to order treatments.
  • Employs ranked lists of treatment performance measures, including smooth conditional means.
  • Incorporates the conditional probability of one treatment dominating others.

Main Results:

  • The proposed method provides an effective ordering of treatments based on performance.
  • Demonstrates robust performance in finite sample empirical studies.
  • Offers a versatile framework for treatment selection.

Conclusions:

  • The novel rank aggregation method enhances personalized treatment selection in crossover trials.
  • This approach is broadly applicable across different models and treatment numbers.
  • Empirical evidence supports the method's efficacy in practical scenarios.