Validating and Generalizing Personalized Treatment Rules by Leveraging Different Data Sources [Methods Study], United States, 2019-2022 (ICPSR 39735)
Version Date: Mar 23, 2026 View help for published
Principal Investigator(s): View help for Principal Investigator(s)
Guanhua Chen, University of Wisconsin-Madison
https://doi.org/10.3886/ICPSR39735.v1
Version V1
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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.
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Study Purpose View help for Study Purpose
To develop statistical methods for creating generalizable PTRs from multiple data sources
Study Design View help for Study Design
The research team developed a weighting method that could use data from a source data set and a target data set to address challenges arising from covariate shifts and confounding. The method is applicable when the source data set includes patient information on covariates, treatment, and health outcomes, but the target data set only includes individual-level data on patient covariates, such as age and comorbid conditions. The new method applied a statistical approach, called sampling weights, to make the covariate distribution in the source data set resemble the covariate distribution in the target data set.
To see if the method could identify which patients would benefit from a specific treatment, the research team tested the method using EHRs from 6,361 patients receiving intensive care for sepsis. They divided the data into source and target data sets. The team first used computer simulations to create six scenarios that mimicked different covariate shifts and confounding. Under each scenario, the team applied the new method to the source data set to develop a generalizable PTR for transthoracic echocardiography as a treatment option for patients with sepsis. Next, the team assessed how generalizable the PTR was for the target data set. They estimated a statistic, called the modified value function, to determine the 28-day mortality rate of the target population under the PTR, compared to the rate based on another PTR that offered a different treatment. The team used the modified value function to compare the PTR developed under the new method with PTRs developed using three other weighting methods.
A patient, a caregiver, a clinician, a nurse, and a director of health system analytics helped design the study.
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Data for 6,361 patients receiving intensive care for sepsis; data were derived from the MIMIC-III database, which includes multiple de-identified EHR databases
Notes
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This study is maintained and distributed by the Patient-Centered Outcomes Data Repository (PCODR). PCODR is the official data repository of the Patient-Centered Outcomes Research Initiative (PCORI).