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Regression-Based Approaches to Patient-Centered Cost-Effectiveness Analysis.

Daisuke Goto1, Ya-Chen Tina Shih2, Pascal Lecomte3

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Summary

Patient-centered cost-effectiveness analyses (CEAs) can be improved using regression methods with patient-level data. These methods account for individual preferences and characteristics, leading to more personalized healthcare decisions.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Health Services Research

Background:

  • Patient centricity is crucial for comprehensive cost-effectiveness analyses (CEAs).
  • Patient-level utility data availability enhances opportunities for patient-centered CEA.
  • Regression-based approaches offer a promising statistical method for patient-centered CEA.

Purpose of the Study:

  • To provide guidance on two regression-based approaches for patient-centered CEA.
  • To utilize patient-level health-related utility and cost data.
  • To enhance CEA by incorporating individual patient characteristics and preferences.

Main Methods:

  • Approach 1: Utilizes patient-reported preferences for patient-specific utility, evaluating the impact of clinical and demographic factors on utility and costs.
  • Approach 2: Estimates patient utilities based on clinically defined health states using a refined Markov transition model for regression-based analysis.
  • Both approaches incorporate patient-level data and regression techniques to analyze utility and cost differences.

Main Results:

  • Regression-based methods can produce CEA estimates reflecting patient-level utilities and costs.
  • These methods allow for adjustments based on socio-demographic and clinical factors.
  • Transparent understanding of utility and cost variations across diverse patient populations is facilitated.

Conclusions:

  • Regression-based approaches enhance CEAs by incorporating patient-level data and preferences.
  • These methods support patient-centered coverage and treatment decision-making.
  • The discussed strategies offer advancements in health services research for analyzing diverse patient populations.