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Data-driven desirability function to measure patients' disease progression in a longitudinal study.

Hsiu-Wen Chen1, Weng Kee Wong2, Hongquan Xu3

  • 1Department of Industrial and Systems Engineering, Chung Yuan Christian University, Taoyuan City 32023, Taiwan.

Journal of Applied Statistics
|March 22, 2016
PubMed
Summary

This study introduces a data-driven method using desirability functions to assess patient treatment response across multiple chronic disease outcomes. The approach minimizes bias and provides a clear overall progression score for better clinical interpretation.

Keywords:
desirability functionlongitudinal datamultiple outcomesnonlinear least squaresscleroderma

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Area of Science:

  • Biostatistics
  • Clinical Trial Methodology
  • Chronic Disease Management

Background:

  • Assessing chronic disease progression often involves multiple outcome measures.
  • Unequal contributions of outcomes and subjective judgments can bias patient response assessments.
  • Standardized methods are needed for robust evaluation of treatment efficacy.

Purpose of the Study:

  • To introduce a data-driven approach using desirability functions for assessing overall patient response to treatment.
  • To minimize bias in outcome assessment by estimating function shapes and weights from a gold standard.
  • To provide a meaningful and interpretable overall progression score for chronic disease patients.

Main Methods:

  • Utilized desirability functions to integrate multiple outcome measures into a single patient assessment.
  • Developed a data-driven strategy to estimate desirability function parameters, mitigating subjective bias.
  • Extended the methodology for longitudinal data analysis with multiple time points.

Main Results:

  • Demonstrated the utility of desirability functions for creating an overall patient response score.
  • The data-driven approach successfully minimized bias in the assessment of chronic disease progression.
  • The method was validated using longitudinal data from a scleroderma clinical trial.

Conclusions:

  • Desirability functions offer a robust framework for evaluating patient response using multiple outcomes in chronic diseases.
  • The proposed data-driven method enhances objectivity and interpretability in clinical trial data analysis.
  • This approach facilitates better comparison and clinical decision-making for patients with chronic conditions.