Bayesian Modeling Framework for Causal Inference and Assessing Sensitivity to Unmeasured Confounding with Multiple Treatments [Methods Study], United States, 2020-2022 (ICPSR 39721)
Version Date: Mar 23, 2026 View help for published
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Liangyuan Hu, Rutgers University
https://doi.org/10.3886/ICPSR39721.v1
Version V1
Summary View help for Summary
The research team based their new method on an existing method called Bayesian Additive Regression Trees, or BART. To test the new method, the team used data created by a computer program to look like real patient data. Then they compared the new method with current methods under different scenarios. Each scenario included three treatments. The team changed the total number of patients, the number of patients who took each treatment, and how alike or different the patients were who took each treatment. Across all scenarios, the team predicted the average treatment effect for all patients and for only patients who received a treatment.
Next, the research team used the new method with real data from patients with lung cancer who were receiving care in New York City hospitals. The team compared three types of surgery: open chest, robotic assisted, and video assisted. The team looked at the effects of each type of surgery on four health outcomes: breathing problems; length of hospital stay after surgery; stay in an intensive care unit, or ICU; and the need to return to the hospital.
Patients, doctors, and researchers helped design the study.
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Study Purpose View help for Study Purpose
To develop a new Bayesian machine learning method for comparing the effects of more than two treatments on a binary outcome
Study Design View help for Study Design
The research team adapted Bayesian Additive Regression Trees to develop a new method for estimating the average treatment effect for the total number of patients (ATE) and the average treatment effect for treated patients (ATT).
In simulation studies, the research team compared the new method with existing methods, estimating ATE and ATT for three treatments options. The team first compared the new method with 10 existing methods in three scenarios that varied the total number of patients and the number of patients receiving each treatment. Then the team compared the new method with two existing methods in four scenarios that varied covariate overlap and confounding.
In the empirical analysis, the research team applied the new method to Surveillance, Epidemiology, and End Results-Medicare (SEER-Medicare) data to compare the effectiveness of three surgical treatments for non-small cell lung cancer on four postsurgical outcomes. The treatments included robotic-assisted surgery, video-assisted thoracic surgery, and open thoracotomy. The team estimated ATT for respiratory complications, prolonged length of hospital stay, intensive care unit stay, and readmission.
Patients, clinicians, and researchers provided input during the study.
Data Source View help for Data Source
SEER-Medicare database; 11,980 Medicare patients ages 65 and older with stage I-IIIA non-small cell lung cancer
Notes
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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).