Methods for the Design and Conduct of Subgroup Analysis in Observational Studies [Methods Study], United States, 2019-2022 (ICPSR 39737)
Version Date: Mar 23, 2026 View help for published
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Laine E. Thomas, Duke University
https://doi.org/10.3886/ICPSR39737.v1
Version V1
Summary View help for Summary
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.
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Study Purpose View help for Study Purpose
To develop and test new methods and tools for subgroup analysis in observational studies
Study Design View help for Study Design
First, the research team created graphical diagnostic plots to help visualize subgroup covariate precision and balance, which is the similarity of baseline characteristics between different treatment groups. The team developed guidance, based on the diagnostic plots, for choosing which PS method to use when designing a subgroup analysis.
Next the team developed a new method called OW-pLASSO by combining the post-LASSO propensity score model with overlap weighting (OW) to improve covariate balance within subgroups and the precision of estimated propensity scores. The team then conducted simulations to compare OW-pLASSO with existing PS methods, such as machine learning models and inverse probability weighting. They measured covariate balance using the absolute standardized mean difference and measured the precision of estimates for different subgroups using relative bias and root mean squared error.
To test the new method, the research team applied OW-pLASSO to data from patients who received a myomectomy or a hysterectomy in the Comparing Options for Management: Patient-Centered Results for Uterine Fibroids (COMPARE-UF) registry. They estimated the effect of each treatment one year post-procedure on mean quality of life score and symptom severity within 35 patient subgroups. The team also compared the estimates from OW-pLASSO with estimates from two existing PS methods.
Clinicians and researchers provided input during the study.
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COMPARE-UF registry data collected between November 11, 2015, and April 18, 2019, from 1,430 women ages 30 and older with uterine fibroids
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