Results Summary

What was the project about?

One goal of comparative effectiveness research is to find out which treatments work best for different groups of patients. For example, treatments may work differently for patients with only one health problem than for those with more than one health problem.

In observational studies, researchers look at health outcomes when patients and their doctors choose the treatments. These studies often use data from electronic health records, or EHRs. Researchers can apply propensity score, or PS, methods to look at different groups of patients. With PS methods, researchers create groups of patients with similar traits who had different treatments. But PS methods require researchers to have data on all patient traits that could affect how well the treatment works. With EHR data, data on some patient traits, like health problems, may be missing. Using current PS methods in observational studies may lead to biased results.

In this study, the research team created new guidance for using PS methods with EHR data to look at the effects of treatment in different groups of patients. The team also created and tested new PS methods to make groups of patients with similar traits.

What did the research team do?

The research team created graphs to show how similar patient traits are within groups when using current PS methods. They used the graphics to develop guidance for planning ways to compare treatments within different groups of patients. Next the team developed a new method called OW-pLASSO for creating groups of patients with similar traits. The team used test data created by a computer to compare OW-pLASSO with current methods.

Then the research team applied OW-pLASSO and two current PS methods to real data from patients who received one of two treatments for uterine fibroids. One year after treatment, the team looked at patient quality of life and symptoms within 35 groups of patients.

Doctors provided input during the study.

What were the results?

With the test data, OW-pLASSO worked better than current methods to create groups of patients with similar traits.

With the real patient data, OW-pLASSO found different treatment effects between patient groups that the current methods didn’t find. For example, patients with mild symptoms before treatment had similar quality of life after getting either treatment. But for patients with moderate to severe symptoms, one treatment worked better than the other to improve quality of life.

What were the limits of the project?

The research team tested OW-pLASSO using data from patients with uterine fibroids. Results may differ with data from patients with other health problems. 

Future research could test how well OW-pLASSO works for patients with other health problems.

How can people use the results?

Researchers can use the results when comparing treatments in patient groups using EHR data. Results from such studies may help patients and their doctors make treatment decisions.

Final Research Report

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

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:

  • The reviewers asked whether the statistical methods developed in this project could be applied to randomized trials as well as to observational studies. The researchers agreed that the methods could benefit randomized trials, particularly in subgroup analyses where imbalances between groups could significantly affect the outcome. The researchers noted that this approach was not part of the current research project, but they did write a paper about this possible use of the new methods and credited this PCORI-funded project for contributing to the ideas relayed in that paper.
  • The reviewers asked for more rationale for the researchers’ use of the LASSO (least absolute shrinkage and selection operator) technique in choosing interaction terms for their statistical model but not using the technique for selecting the main effects of covariates in the model. The reviewers felt that reducing the number of covariates in the model by removing those with little or no effect on the outcome would reduce the statistical model’s complexity. The researchers added an explanation to the report, clarifying that their goal was to specify a propensity score model that took into consideration the potential for covariate-subgroup interaction rather than assuming that the interaction would be 0. The LASSO technique was therefore used to identify the most salient covariate-subgroup interactions to include in their propensity score models.

Conflict of Interest Disclosures

Project Information

Laine E. Thomas, PhD
Duke University
Methods for the Design and Conduct of Subgroup Analysis in Observational Studies

Key Dates

April 2019
September 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: March 14, 2024