Bayesian Hierarchical Models for the Design and Analysis of Studies to Individualize Healthcare [Methods Study], United States, 2015-2020 (ICPSR 39613)
Version Date: Dec 11, 2025 View help for published
Principal Investigator(s): View help for Principal Investigator(s)
Scott L. Zeger, Johns Hopkins University
https://doi.org/10.3886/ICPSR39613.v1
Version V1
Summary View help for Summary
When choosing a treatment, doctors often look at research results that show how well the treatment worked in large groups of people. But many factors can affect how well a treatment works for an individual patient. These factors may include the patient's sex, age, other health problems, or how they responded to treatments in the past. Some patient data sources, such as electronic health records, have this information. But existing statistical methods may not use these data well. For example, existing methods may not be able to take advantage of data that include measurements of a patient's health from more than one point in time.
For this project, the research team developed new methods to analyze data that includes measurements of a patient's health from different points in time. To develop the new methods, the team used a Bayesian approach. Bayesian approaches include findings from previous studies in the analysis, which can make results more accurate.
To access the software and methods, please visit the Neuroconductor website and neuroc_travis GitHub.
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Study Purpose View help for Study Purpose
To use a Bayesian analytic approach to develop and implement new methods and software for predicting individual patient health status, changes in health status over time, and response to treatment.
Study Design View help for Study Design
The research team developed a Bayesian hierarchical statistical model. The model had four components:
- Estimation of the effects of current treatment and patient characteristics such as age and clinical history on health status
- Use of multiple clinical measures to infer health status
- Analysis of the effect of health measurements at one time point on subsequent treatment decisions
- Specification of a model with two levels in which factors affecting the estimation of treatment effects could vary over time for the same patient and could also vary across many patients with similar characteristics
The research team then developed a software package called OSLER inHealth to help other researchers implement the Bayesian hierarchical model as well as existing methods to make individual predictions on health status and treatment effects.
Finally, the research team used three case studies to test and adapt the statistical model and software to predict a patient's health status and changes in health over time. Patients, clinicians, and a health plan administrative leader helped build and refine the statistical models for each case study.
Data Source View help for Data Source
Data from 3 clinical studies, including (1) the PERCH Study on childhood pneumonia, (2) the Brady Urological Institute active surveillance study on prostate cancer, and (3) the Janssen schizophrenia trial and National Network of Depression Centers project
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