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Operant Protocols for Assessing the Cost-benefit Analysis During Reinforced Decision Making by Rodents

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Published on: September 10, 2018

Using causal forests to assess heterogeneity in cost-effectiveness analysis.

Carl Bonander¹, Mikael Svensson¹

¹School of Public Health and Community Medicine, Institute of Medicine, University of Gothenburg, Gothenburg, Sweden.

Health Economics

|May 4, 2021

Summary

This study introduces a data-driven method to analyze cost-effectiveness analysis (CEA) heterogeneity using causal forests. The approach estimates variations in outcomes and costs, aiding personalized healthcare decisions.

Keywords:

causal forest cost-effectiveness analysis machine learning stratified analysis treatment heterogeneity

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Area of Science:

Health Economics
Biostatistics
Machine Learning

Background:

Cost-effectiveness analysis (CEA) traditionally provides average treatment effects, potentially masking important patient subgroup variations.
Understanding heterogeneity in CEA is crucial for personalized medicine and optimizing resource allocation.
Existing methods for analyzing heterogeneity in individual-level data are limited.

Purpose of the Study:

To develop and validate a data-driven methodology for estimating and analyzing heterogeneity in CEA.
To provide tools for exploring variations in incremental outcomes, costs, and net monetary benefits across patient subgroups.
To enable the learning of data-driven optimal policy rules for treatment decisions.

Main Methods:

Utilized causal forests for estimating heterogeneous treatment effects on outcomes and costs.
Employed cross-fitted augmented inverse probability weighted learning for robust estimation.
Developed visualizations, including individual-level cost-effectiveness planes, for heterogeneity analysis.

Main Results:

Demonstrated the ability to estimate average cost-effectiveness in the overall sample and specific subpopulations.
Successfully explored and analyzed heterogeneity in incremental outcomes, costs, and net monetary benefits.
Showcased the identification of determinants of heterogeneity and the derivation of data-driven policy rules.

Conclusions:

The proposed data-driven method effectively estimates and analyzes heterogeneity in CEA using individual-level data.
Causal forests and related machine learning techniques offer powerful tools for personalized health economic evaluations.
This approach facilitates more nuanced decision-making in healthcare by accounting for patient-specific cost-effectiveness.