Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach

Sparse data bias, where there is a lack of sufficient cases, is a common problem in data analysis, particularly when studying rare binary outcomes. Although a two-step meta-analysis approach may be used to lessen the bias by combining the summary statistics to increase the number of cases from multiple studies, this method does not completely eliminate bias in effect estimation. In this paper, we propose a one-shot distributed algorithm for estimating relative risk using a modified Poisson regression for binary data, named ODAP-B. We evaluate the performance of our method through both simulation studies and real-world case analyses of postacute sequelae of SARS-CoV-2 infection in children using data from 184 501 children across eight national academic medical centers. Compared with the meta-analysis method, our method provides closer estimates of the relative risk for all outcomes considered including syndromic and systemic outcomes. Our method is communication-efficient and privacy-preserving, requiring only aggregated data to obtain relatively unbiased effect estimates compared with two-step meta-analysis methods. Overall, ODAP-B is an effective distributed learning algorithm for Poisson regression to study rare binary outcomes. The method provides inference on adjusted relative risk with a robust variance estimator.

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Publication Details

Journal
Statistics in Medicine
Published
2024-11-03
DOI
https://doi.org/10.1002/sim.10250
Citations
2
Primary Topic
COVID-19 epidemiological studies
Type
article
Field-Weighted Citation Impact
0.79

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article

Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach

Haitao Chu, Christopher B. Forrest, Jiayi Tong, X. C. Lou et al.
2 citations
Statistics in Medicine
COVID-19 epidemiological studies
0.79
article

Advancing Interpretable Regression Analysis for Binary Data: A Novel Distributed Algorithm Approach

Haitao Chu, Christopher B. Forrest, Jiayi Tong, X. C. Lou, Vitaly Lorman, Ravi R. Jhaveri, Jiang Bian, Jenna Reps, Lu Li, Mackenzie Edmondson, Nathan M. Pajor, Kelly J. Kelleher, Naimin Jing, Yong Chen
article en
2 citations

Abstract

Sparse data bias, where there is a lack of sufficient cases, is a common problem in data analysis, particularly when studying rare binary outcomes. Although a two-step meta-analysis approach may be used to lessen the bias by combining the summary statistics to increase the number of cases from multiple studies, this method does not completely eliminate bias in effect estimation. In this paper, we propose a one-shot distributed algorithm for estimating relative risk using a modified Poisson regression for binary data, named ODAP-B. We evaluate the performance of our method through both simulation studies and real-world case analyses of postacute sequelae of SARS-CoV-2 infection in children using data from 184 501 children across eight national academic medical centers. Compared with the meta-analysis method, our method provides closer estimates of the relative risk for all outcomes considered including syndromic and systemic outcomes. Our method is communication-efficient and privacy-preserving, requiring only aggregated data to obtain relatively unbiased effect estimates compared with two-step meta-analysis methods. Overall, ODAP-B is an effective distributed learning algorithm for Poisson regression to study rare binary outcomes. The method provides inference on adjusted relative risk with a robust variance estimator.

Statistics in MedicineVol. 43(29)
Cincinnati Children's Hospital Medical Center (US), Lurie Children's Hospital (US), Nationwide Children's Hospital (US), Children's Hospital of Philadelphia (US), Johns Hopkins University (US), Pfizer (United States) (US), University of Florida Health (US), Erasmus MC (NL), University of Florida (US), Penn Center for AIDS Research (US), Applied Mathematics (United States) (US), Decision Sciences (United States) (US), University of Pennsylvania Health System (US), Janssen (United States) (US), Statistical Research (United States) (US), University of Cincinnati (US), University of Pennsylvania (US)
Patient-Centered Outcomes Research Institute, National Institutes of Health
Openalex Percentile: Top 29%
COVID-19 epidemiological studies
0.79
DOIOpen Access PDFOpenAlex
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