Methods for Heterogeneity of Treatment Effects: Random Forest Counterfactual Machines [Methods Study], Cleveland, Ohio, 2014-2019 (ICPSR 39559)
Version Date: Nov 24, 2025 View help for published
Principal Investigator(s): View help for Principal Investigator(s)
Daniel J. Feaster, University of Miami
https://doi.org/10.3886/ICPSR39559.v1
Version V1
Summary View help for Summary
Patients may respond differently to the same treatment due to individual traits such as age or gender. Knowing how different traits can affect a patient's response to treatment can help doctors and patients make better treatment decisions. For example, this information can help doctors know what types of cancer medicines work better for certain patients. This project focuses on improving the methods that researchers use to compare how treatments work for different patients.
In this project, the research team developed and tested a statistical method called random forests, or RF. RF is a way to analyze data using a technique called machine learning. In machine learning, computers use data to learn how to perform different tasks with little or no human input. Many types of RF methods exist. The team compared multiple RF methods to learn how well the methods would work to find out how patients with different traits respond to the same treatment.
To access the R package, please visit the randomForestSRC CRAN webpage.
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Study Purpose View help for Study Purpose
To develop and evaluate random forest methods for estimating individual treatment effects. Specifically, aim 1 was to develop comprehensive methodology (a) to estimate individual TEs (ITEs) for patients using counterfactual random forest (RF) machines for heterogeneous data, which (b) work across differing magnitudes of HTE, when (c) data are observational with potential confounding, and (d) with survival outcomes and multiple treatment comparisons. Aim 2 was to develop software to implement these new methods in a wide array of comparative effectiveness research applications by adding functionality to the existing CRAN-distributed randomForestSRC package developed by our group, a package in its fifth release at the beginning of this project.
Study Design View help for Study Design
Researchers tested multiple RF methods in four studies, including three studies using simulated data sets and one study using empirical data. In the first study using data that simulated randomized controlled trials, they compared RF to other methods of estimating individual treatment effects, including multiple imputation and Bayesian Additive Regression Trees (BART). In the remaining studies, they tested the performance of various RF methods in scenarios varying the magnitude of HTE in randomized trials, using potentially confounded observational data, and accommodating survival outcomes and multiple treatment comparisons.
An advisory board made up of public health department representatives, infectious disease doctors, and community researchers provided input during the study.
Universe View help for Universe
Patients who were treated for ischemic cardiomyopathy at the Cleveland Clinic
Data Source View help for Data Source
Simulated data, observational data from 1468 patients who were treated for ischemic cardiomyopathy
Notes
The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.
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This study is maintained and distributed by the Patient-Centered Outcomes Data Repository (PCODR). PCODR is the official data repository of the Patient-Centered Outcomes Research Initiative (PCORI).