Matching Complex Patients to Treatments: Innovative Statistical Scoring Methods for Treatment Selection [Methods Study], 2015-2020 (ICPSR 39580)
Version Date: Nov 24, 2025 View help for published
Principal Investigator(s): View help for Principal Investigator(s)
Menggang Yu, University of Wisconsin
https://doi.org/10.3886/ICPSR39580.v1
Version V1
Summary View help for Summary
Patients may respond differently to the same treatment due to differences in personal traits such as age, gender, or the number and type of health problems they have. Researchers use statistical methods to predict how well a treatment may work for patients based on their personal traits. But current methods may not work well if patients have many health problems or are taking other medicines.
In this project, the research team created new methods to figure out which patient traits are related to treatment benefits to help doctors and patients understand the likely treatment benefits for individual patients.
To access the methods, software, and R package, please visit the personalized CRAN webpage and personalized GitHub
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Study Purpose View help for Study Purpose
To develop and test methods for estimating HTE for different patient characteristics to rank treatment benefits for patients. Researchers had 3 specific aims: aim 1 was to develop treatment scoring systems using existing single clinical trial data sets with outcomes collected longitudinally. Aim 2 was to develop treatment scoring systems using existing data sets from multiple clinical trials. Aim 3 was to develop treatment scoring systems using existing data sets from large-scale observational studies.
Study Design View help for Study Design
To measure HTE, researchers estimated the treatment benefit for individual patients, also known as the individual treatment effect (ITE), based on different patient characteristics.
First, researchers developed a general statistical framework for estimating ITE to identify patient characteristics that moderate treatment outcomes. Using different estimation methods, researchers modeled treatment assignment rather than treatment outcomes. They included outcomes as weights to examine the effect of interactions between treatments and patient characteristics on various treatment assignments.
Using simulation studies, researchers evaluated the framework and tested the performance of different estimation methods. Researchers then used the ITE estimates to create treatment benefit scores. The scores indicated a treatment's benefit based on the influence of patient characteristics. Researchers used the scores to rank treatment benefits for individual patients based on their specific patient characteristics.
Researchers tested the methods using data from an observational study to identify which patient characteristics were related to benefits from a chronic care management intervention. They adapted the methods for three separate longitudinal outcomes.
Researchers worked with members of a health system management team, a patient, a caregiver, patient advocates, biostatisticians, and clinician researchers.
Data Source View help for Data Source
Simulated data, observational data
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).