Results Summary

What was the project about?

Patients often have more than two treatment options. Researchers can use data from electronic health records, or EHRs, to compare different treatments and to learn about what treatment works best for patients. But current statistical methods don’t work well for comparing more than two treatments. Also, patients using different treatments may have different traits like age or number of health problems, which can bias results.

In this study, the research team created and tested a new method for using EHR data to compare more than two treatments. To develop the new methods, the team used a Bayesian approach. Bayesian approaches include findings from previous studies in the analysis, which can make results more accurate.

What did the research team do?

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.

What were the results?

With the test data, the new method was more accurate than current methods when looking at the average treatment effect for all patients. The new method worked about the same as current methods when looking at only patients who received a treatment.

When using real patient data, the new method had the most precise predictions compared to other methods. The results showed that one treatment, robotic-assisted surgery, worked better than other surgery types to lower risk of a long hospital stay or an ICU stay. The risk of breathing problems or needing to return to the hospital were similar across types of surgery.

What were the limits of the project?

The research team tested the new method using data from patients who have Medicare. Results may differ for other patients. The team used data from one time point. Results may differ for studies that look at patient outcomes over time.

Future research could test how well the new method works for patient outcomes over time and for patients with other health problems.

How can people use the results?

Researchers can use the methods for studies comparing more than two treatments using EHR data.

Final Research Report

This project's final research report is expected to be available by May 2024.

Peer-Review Summary

The Peer-Review Summary for this study will be posted here soon.

Project Information

Liangyuan Hu, PhD
Rutgers, The State University of New Jersey
Bayesian Additive Regression Trees for Causal Inference with Multiple Treatments and a Binary Outcome

Key Dates

August 2018
May 2023

Study Registration Information


Has Results
Award Type
Intervention Strategy Intervention Strategies PCORI funds comparative clinical effectiveness research (CER) studies that compare two or more options or approaches to health care, or that compare different ways of delivering or receiving care. View Glossary
State State The state where the project originates, or where the primary institution or organization is located. View Glossary
Last updated: October 18, 2023