One of PCORI’s goals is to improve the methods that researchers use for patient-centered outcomes research. PCORI funds methods projects like this one to better understand and advance the use of research methods that improve the strength and quality of comparative effectiveness research.
This research project is in progress. PCORI will post the research findings on this page within 90 days after the results are final.
What is the project about?
Researchers can use data in electronic health records, or EHRs, to compare two treatments to each other. Doing so can help show which treatment works better for patients. But comparing more than two treatments can be hard. Patients who get different treatments may differ from each other. Current methods for analyzing data may not fully account for differences among patients when many treatments are available to choose from, especially if information on patient traits is missing from EHRs.
In this study, the research team is developing new methods that let researchers make accurate comparisons across many treatments. The new methods also assess how conclusions will vary when information is missing from EHRs.
How can this project help improve research methods?
Results may help researchers when considering methods to compare multiple treatment options.
What is the research team doing?
In this project, the research team is using machine learning techniques to compare multiple treatments. In machine learning, computers use data to learn how to perform tasks with little or no human input. The team is creating data to see how the new machine learning methods work in different settings. The team is also comparing the new methods to current methods. In addition, the team is testing how well the new methods account for traits that aren’t included in EHRs. Finally, the team is applying the new methods to existing data comparing treatment options for lung cancer.
Research methods at a glance
The aims of this study are to
|Approach||Bayesian machine learning techniques involving Bayesian Additive Regression Trees (BART); simulation analyses|