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Related Concept Videos

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Related Experiment Video

Updated: Sep 18, 2025

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Personalized Treatment Selection for Multivariate Ordinal Scale Outcomes and Multiple Treatments.

Chathura Siriwardhana1, Bakeerathan Gunaratnam2, K B Kulasekera2

  • 1Department of Quantitative Health Sciences, University of Hawaii John A. Burns School of Medicine, Honolulu, Hawaii, USA.

Pharmaceutical Statistics
|June 24, 2025
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Summary

This study introduces a novel method for personalized treatment selection using correlated ordinal responses. It employs rank aggregation to optimize treatment decisions based on individual patient data and preferences.

Keywords:
ordinal responsespersonalized treatmentsrank aggregationsemiparametric regression

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Area of Science:

  • Biostatistics
  • Clinical Trial Design
  • Personalized Medicine

Background:

  • Selecting optimal treatments with multiple, correlated outcomes (especially ordinal) is challenging.
  • Existing methods may not adequately handle the complexity of ordinal response scales and interdependencies.

Purpose of the Study:

  • To develop an innovative, individualized approach for treatment selection with correlated multiple ordinal responses.
  • To provide a flexible framework accommodating various models, treatments, and responses, including weights for customization.

Main Methods:

  • Utilized rank lists generated from conditional probabilities of ordinal outcomes.
  • Introduced a rank aggregation technique to combine multiple rank lists, accounting for within- and between-list correlations.
  • Incorporated response weights for patient- and clinician-driven customization.

Main Results:

  • A simulation study demonstrated the method's performance in finite samples.
  • Illustrative examples from Cystic Fibrosis and Alzheimer's Disease clinical trials showcased practical application.

Conclusions:

  • The proposed method offers a versatile and adaptable approach to individualized treatment selection.
  • It effectively handles correlated multiple ordinal responses, enhancing clinical decision-making in complex scenarios.