Statistical Methods for Development, Validation, and Implementation of Absolute Risk Models [Methods Study], 2016-2022 (ICPSR 39730)
Version Date: Mar 12, 2026 View help for published
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Nilanjan Chatterjee, Johns Hopkins University
https://doi.org/10.3886/ICPSR39730.v1
Version V1
Summary View help for Summary
Factors, such as personal traits, behaviors, or the environment, can affect a person's risk of getting an illness. Doctors can use risk models, which account for these factors, to predict a person's chance of getting an illness. The risk models group patients into different levels for certain illnesses, such as high risk or low risk.
Most risk models look at only a small number of factors, which affects how well the models can separate patients into different levels. Combining factors from different studies into a single risk model may improve how well the model works. Researchers can use statistical methods to combine data from different studies. But current methods don't work when the studies look at different traits or other factors.
In this study, the research team developed a new method for combining data from studies that have information on different risk factors. The new method is called Generalized Meta-Analysis, or GENMETA.
To access the R package, please visit the Implements Generalized Meta-Analysis Using Iterated Reweighted Least Square Algorithm CRAN webpage.
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Study Purpose View help for Study Purpose
To develop a new method for multivariate meta-analysis using summary-level information across studies with different covariates
Study Design View help for Study Design
The research team developed a new method, called Generalized Meta-Analysis (GENMETA), for estimating parameters of a multiple regression model through meta-analysis of studies with summary-level information that use different covariates. To verify the effectiveness of the new method, the team used a series of simulation studies to show that the method is robust even when underlying assumptions are violated. They also proposed a model diagnostic test to detect violations of model assumptions due to heterogeneity.
Next, the research team used GENMETA to develop models for predicting the risk of breast cancer by combining information from the Breast Prostate Colorectal Cancer Cohort study and the Breast Cancer Detection and Demonstration Project studies, which include different risk factors. The team first used the model diagnostic test to show that the underlying model assumption was unlikely to be violated. Then the team applied GENMETA to the two studies to carry out the meta-analysis. Results showed that the GENMETA method could produce estimates for all risk factors from the two different studies that were related to breast cancer.
The research team developed a software package to implement GENMETA and shared it online at no cost to users.
Data Source View help for Data Source
Simulated data based on two datasets: 7,448 cases and 8,812 controls from the Breast Prostate Colorectal Cancer Cohort study
1,217 cases and 1,616 controls from the Breast Cancer Detection and Demonstration Project"
Notes
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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).