Patient Centered Adaptive Treatment Strategies (PCATS) Using Bayesian Causal Inference [Methods Study], 2015-2020 (ICPSR 39520)

Version Date: Oct 21, 2025

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Principal Investigator(s):
Bin Huang, Cincinnati Children's Hospital Medical Center

https://doi.org/10.3886/ICPSR39520.v1

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Summary

Treatment plans for patients with long-term health problems such as diabetes or arthritis often change over time. Such plans are called adaptive treatment plans as doctors adapt treatment based on the patient's health problem and response to earlier treatments. Adaptive treatment plans are common, but the methods to assess how well a plan works may not always provide accurate results. To know which plans are best for patients, researchers need better methods to compare these adaptive plans.

In this study, the research team developed and tested a new statistical method and looked at whether it could more accurately compare adaptive treatment plans.

To access the methods and software, please visit the PCATS Application.

Citation

Huang, Bin. Patient Centered Adaptive Treatment Strategies (PCATS) Using Bayesian Causal Inference [Methods Study], 2015-2020. Inter-university Consortium for Political and Social Research [distributor], 2025-10-21. https://doi.org/10.3886/ICPSR39520.v1

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Funding

Patient-Centered Outcomes Research Institute (PCORI) (ME-1408-19894)

Subject Terms

Distributor(s)

Inter-university Consortium for Political and Social Research
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Time Period(s)

2015 -- 2020
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Study Purpose

Simulated data, observational data from electronic health records from a pediatric rheumatology clinic. The project aims were: Aim 1. To develop, refine, and disseminate bayesian causal inference methods for evaluating clinical effectiveness and for informing better PCATS. Aim 2. To evaluate the clinical effectiveness of the recommended ATS for patients with pcJIA using real-world data

Study Design

The research team developed a causal inference method called GPMatch that combines two statistical techniques, Gaussian process covariance function matching and Bayesian nonparametric modeling, in one step.

Using simulated data, the research team compared GPMatch against Bayesian additive regression trees (BART) and other commonly used statistical methods for adaptive and non-adaptive treatment strategies. To test the robustness of the methods in the presence of model misspecification, the team intentionally introduced error into the statistical models.

The research team then applied GPMatch to empirical data from electronic health records. The team compared the effectiveness of two adaptive treatment strategies for children with polyarticular-course juvenile idiopathic arthritis (pcJIA):

The early combination plan, in which patients start two types of medicine after diagnosis The step-up plan, in which patients start with one type of medicine and then add another type later

Universe

Pediatric rheumatology patients

Data Source

Simulated data, observational data from electronic health records from a pediatric rheumatology clinic

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Original Release Date

2025-10-21

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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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