Filling Two Major Gaps in the Analysis of Heterogeneity of Treatment Effects for Patient-Centered Outcomes Research [Methods Study], 2013-2018 (ICPSR 39522)
Version Date: Oct 21, 2025 View help for published
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Ravi Varadhan, Johns Hopkins University
https://doi.org/10.3886/ICPSR39522.v1
Version V1
Summary View help for Summary
Comparative effectiveness research compares two or more treatments to see which one works better for which patients. Sometimes, groups of people respond differently to the same treatment. For example, women might, on average, receive more benefit from a treatment than men do. If researchers group women and men together when they analyze study data, they may miss this difference and overlook some of the benefits of a treatment.
Researchers can analyze data on the effects of a treatment in many ways. Each way has strengths and weaknesses. Bayesian regression is one method that allows researchers to consider various factors in their analysis, such as patients' ages, sex, or health problems. This method can help researchers understand how different groups of people respond to a treatment. But it requires advanced computer programs that are not readily available to all researchers.
In this study, the research team wanted to make it easier for researchers to use Bayesian regression.
To access the R package, please visit the Beanz CRAN webpage.
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Study Purpose View help for Study Purpose
(1) To develop recommendations for assessing heterogeneity of treatment effects (HTE) using Bayesian regression along with a corresponding user-friendly, open-source, validated software suite to perform the analyses; (2) To develop recommendations for the choice of treatment-effect scale for the assessment of HTE
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The term HTE describes a situation in which individuals have different responses to a treatment; for example, some receive a benefit while others do not or are harmed. HTE can occur across a variety of patient characteristics, such as demographics, health behavior, genetics, or comorbidity. Researchers often use subgroup analysis to determine whether HTE exists, but this approach is imprecise and may elevate error rates. Using Bayesian regression models helps overcome these limitations by formally including prior information about the subgroup in the estimation process and then updating the likelihood that people in a subgroup will respond a certain way to treatment as more information becomes available. However, many researchers may not be familiar with Bayesian models, which require complex software.
For objective 1, the research team developed specific analytic recommendations for using Bayesian regression to evaluate HTE. Then the team developed a free, user-friendly software package, called beanz, for comprehensive Bayesian HTE analysis. A panel of leading statisticians and methodologists provided input on the analytic recommendations and the software package.
For objective 2, the research team conducted a literature review and elicited guidance from the expert panel to develop recommendations related to selecting the appropriate treatment-effect scale for HTE analysis and reporting of results. However, the beanz software currently provides HTE analysis only on commonly used relative scales (e.g., risk ratios or odd ratios).
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Data from a clinical trial: The studies of left ventricular dysfunction (SOLVD) DOI: 10.1056/NEJM199209033271003
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.