Results Summary

What was the project about?

Researchers can use data on patient traits such as age, health problems, and treatment preferences, to create personalized treatment rules, or PTRs. PTRs provide doctors with guidance on how to treat patients’ health problems based on their traits. But PTRs based on a single data source may not apply to all patients. For example, if researchers create a PTR using data from older people with heart failure, it may not apply to younger people with heart failure.

To avoid this problem, researchers can create PTRs by combining data from many sources. PTRs based on many data sources can help guide treatment for patients with different traits.

In this study, the research team created and tested a new method for creating PTRs using data from multiple sources.

What did the research team do?

First, the research team developed the new method for creating a PTR that applies to many different patients. The method uses data from a source data set and a target data set.

Next, the research team tested the new method. They used health record data from 6,361 patients who received intensive care for sepsis, or wide-spread infection in the body. The team created source and target data sets. Using the source data set, they applied the new method to create a PTR for sepsis treatment. Then they used a computer program to check how well the PTR worked with the target data set under six scenarios. For example, they checked how the method worked when the patient traits between the two data sets were similar or different. They also compared the new method with three existing methods.

A patient, a caregiver, a doctor, a nurse, and an expert in data analysis helped design the study.

What were the results?

Overall, the new method performed better than other methods. The method also worked better than other methods when patient traits in the source and target data sets differed a lot.

What were the limits of the project?

If data on certain patient traits that affect treatment are not in the data sets, then the new method will not create an accurate PTR. If the data reflects bias in how patients of different races and ethnicities receive care, then PTRs may not improve health for all patients.

Future research could look at how well the method works for creating PTRs for patients of different races and ethnicities.

How can people use the results?

Researchers can use the new method to create PTRs using patient data from many sources. These PTRs could help doctors create treatment plans that can work for different patients.

Final Research Report

View this project's final research report.

Peer-Review Summary

Peer review of PCORI-funded research helps make sure the report presents complete, balanced, and useful information about the research. It also assesses how the project addressed PCORI’s Methodology Standards. During peer review, experts read a draft report of the research and provide comments about the report. These experts may include a scientist focused on the research topic, a specialist in research methods, a patient or caregiver, and a healthcare professional. These reviewers cannot have conflicts of interest with the study.

The peer reviewers point out where the draft report may need revision. For example, they may suggest ways to improve descriptions of the conduct of the study or to clarify the connection between results and conclusions. Sometimes, awardees revise their draft reports twice or more to address all of the reviewers’ comments. 

Peer reviewers commented and the researchers made changes or provided responses. Those comments and responses included the following:

  • Reviewers noted that the methods developed in this study to create personalized treatment recommendations (PTR) did not adequately account for unmeasured confounding and patient treatment choice based on the expected gains for each patient. They recommended that the researchers create a conceptual model for their study and link back to it throughout the report. The researchers explained that their project assumes there is no unmeasured confounder, and that that this study was not designed to consider unmeasured confounders, so the reviewers’ suggestions were beyond the scope of this study. Rather than preparing a conceptual model, the researchers referred the reviewers to their potential outcome notation, Y(t), which is used throughout the report.
  • The reviewers asked the researchers to explain their purpose in adding data across multiple data sources, commenting that multiple data sources would not solve the problem of estimating treatment outcomes in observational data. The researchers explained that combining multiple data sources improved the generalizability and stability of their PTR model.

Conflict of Interest Disclosures

Project Information

Guanhua Chen, PhD
University of Wisconsin School of Medicine and Public Health
Validating and Generalizing Personalized Treatment Rules by Leveraging Different Data Sources

Key Dates

April 2019
March 2023

Study Registration Information


Has Results
Award Type
Health Conditions Health Conditions These are the broad terms we use to categorize our funded research studies; specific diseases or conditions are included within the appropriate larger category. Note: not all of our funded projects focus on a single disease or condition; some touch on multiple diseases or conditions, research methods, or broader health system interventions. Such projects won’t be listed by a primary disease/condition and so won’t appear if you use this filter tool to find them. View Glossary
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: August 15, 2023