Bayesian Modeling Framework for Causal Inference and Assessing Sensitivity to Unmeasured Confounding with Multiple Treatments [Methods Study], United States, 2020-2022

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.