Stratified Regression Models for Case-Only Studies [Methods Study], Massachusetts, 2014-2022 (ICPSR 39710)
Version Date: Mar 23, 2026 View help for published
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Murray A. Mittleman, Harvard University
https://doi.org/10.3886/ICPSR39710.v1
Version V1
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One way to see if a treatment works is to compare data from people who received the treatment with data from those who didn't or who received a different treatment. But sometimes the ways that people differ, such as their age or other health problems, can bias results. For example, if the people who didn't get the treatment are older or sicker than people who did get the treatment, results could suggest that the treatment works better than it really does.
One way to avoid this type of bias is to use case-only study designs. Case-only studies compare each patient's health before and after treatment. But case-only studies often report the relative risk of a health event, such as stroke, among two groups of patients, instead of the absolute risk. For example, relative risk can show how the risk of stroke differs between patients who smoke and those who do not. Absolute risk would give the percentage of patients having a stroke among all patients. Absolute risk can help inform treatment decisions. But methods to measure absolute risk in case-only studies are limited. Also, clear guidance is lacking on how to best design and analyze a case-only study.
In this study, the research team created a guide and new methods for designing and analyzing case-only studies.
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Study Purpose View help for Study Purpose
- Aim 1: Develop analytic techniques to estimate measures of absolute effect in case-only studies, including number needed to treat or harm, and to evaluate the presence of heterogeneity of treatment effects on the additive scale.
- Aim 2: Develop methods for addressing within-person, time-varying confounding in case-only studies using weights for the probability of exposure
- Aim 3: Use stakeholder input to create SAS Macros, Stata .ado files, and R packages with relevant help files, and tutorials to estimate relative and absolute risks with output optimized to help clinicians and patients make informed decisions.
- Aim 4: Develop methods guidance for case-only designs in patient-centered outcomes research by rigorously comparing the validity, efficiency, assumptions, and ability to identify heterogeneity of treatment effects for each of the case-only designs using simulations and empirical clinically relevant examples
Study Design View help for Study Design
To develop guidance for selecting and analyzing a case-only design, the research team analyzed simulated data and compared the validity and efficiency of findings from six case-only designs. The team examined the potential bias when statistical assumptions of case-only designs were and were not met. For example, case-only designs assume that confounders and the probability of receiving treatment do not change over time. Based on the simulation results, the team described data and design considerations for case-only studies that worked when different statistical assumptions were relaxed.
To estimate absolute risk and heterogeneity of treatment effects, the research team developed new methods that account for differences in changes over time in the probability of treatment and health outcomes.
The research team provided guidance about how to select an appropriate case-only design to best address the research question of interest, corresponding statistical assumptions, and data considerations.
Doctors and patients provided input that helped in designing the study.
Data Source View help for Data Source
Beth Israel Deaconess Medical Center (BIDMC) electronic medical record database
Notes
The public-use data files in this collection are available for access by the general public. Access does not require affiliation with an ICPSR member institution.
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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).