Propensity Score-Based Methods for Clinical Evaluation Report (CER) Using Multilevel Data: What Works Best When [Methods Study], 2014-2019 (ICPSR 39574)
Version Date: Nov 20, 2025 View help for published
Principal Investigator(s): View help for Principal Investigator(s)
Mi-Ok Kim, University of California-San Francisco
https://doi.org/10.3886/ICPSR39574.v1
Version V1
Summary View help for Summary
This project aims to improve the methods that researchers use to compare how treatments affect different patients. When researchers use data from patients' health records to compare treatments, it's often hard to know whether changes in a patient's health are from the treatment or something else. Factors other than the treatment may affect the patient's health, including
- A patient's traits, such as age, gender, or other health problems
- Group-level factors, such as where patients get care or where they live
To address this problem, researchers rely on statistical methods. Existing methods use data from patients who have similar traits but received different treatments. But they may not work well if some group-level factors affect both the treatment and patients' health. In this study, the research team created two new ways of including group-level factors in the methods they use to find similar patients.
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Study Purpose View help for Study Purpose
To develop PS matching methods for hierarchical data.
- Aim 1: Investigate how to extend the PS methodology and compare PS matching methods under various assumptions.
- Aim 2: Develop a novel imputation-based sensitivity analysis approach.
- Aim 3: Identify valid and efficient PS methods for 2 existing comparative effectiveness research studies and conduct the sensitivity analysis using the imputation-based approach developed under aim 2.
Study Design View help for Study Design
The research team simulated data to mimic the characteristics of real-world hierarchical data. They considered four PS matching methods:
- Across-cluster matching, which ignored the hierarchical data structure
- Within-cluster matching, which accounted for the hierarchical data structure
- Stratified matching, which stratified data by a cluster-level variable, such as hospital size, and then matched patients across clusters within strata to ensure closer matches
- Hierarchical matching, which grouped clusters with similar cluster-level variables within each stratum and matched patients across clusters within each group
To assess the performance of each method, the research team examined bias and the root mean squared error (RMSE) of the estimated treatment effects. The team also compared matched sample sizes to determine if the matching methods would produce sufficiently large matched samples for PS analysis.
A patient and caregiver advocate, a clinician researcher, and a research foundation representative helped design and conduct the study.
Universe View help for Universe
Pediatric kidney transplant patients; pediatric patients with newly diagnosed Crohn's disease (RISK study)
Data Source View help for Data Source
Simulations based on real-world comparative effectiveness research (CER) studies, data from two existing CER studies
Notes
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