Propensity Score-Based Methods for Clinical Evaluation Report (CER) Using Multilevel Data: What Works Best When [Methods Study], 2014-2019 (ICPSR 39574)
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.