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Estimating Costs Associated with Disease Model States Using Generalized Linear Models: A Tutorial.

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This tutorial guides healthcare cost estimation for decision analytic models using patient-level data. It provides a practical, step-by-step approach for modeling healthcare costs associated with specific disease states.

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Area of Science:

  • Health Economics
  • Biostatistics
  • Health Services Research

Background:

  • Decision analytic models require accurate disease cost estimates to evaluate interventions.
  • Patient-level data and heterogeneity are increasingly used in modeling, demanding individualized cost data.
  • Healthcare cost data present unique statistical challenges, including numerous zeros and skewed distributions.

Purpose of the Study:

  • To provide practical guidance on estimating healthcare costs for decision analytic models.
  • To present a step-by-step guide using individual participant data for cost estimation.
  • To address the lack of practical guidance in cost estimation for decision modeling.

Main Methods:

  • Utilizes the generalized linear model (GLM) framework for cost modeling.
  • Focuses on practical aspects from research question conceptualization to cost derivation.
  • Includes a practical example with R code for modeling hospital costs in cardiovascular disease.

Main Results:

  • Demonstrates how to use patient-level data to estimate costs over discrete periods.
  • Provides a framework for modeling healthcare costs associated with specific disease states.
  • Illustrates the application of GLM for cost estimation in a cardiovascular disease context.

Conclusions:

  • Offers a practical, step-by-step guide for estimating healthcare costs using patient-level data.
  • The GLM framework is presented as a suitable method for modeling complex healthcare cost data.
  • This guidance supports the development of more accurate and individualized decision analytic models.