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

Autor: Tong J; Center for Health AI and Synthesis of Evidence (CHASE), Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA., Li L; Center for Health AI and Synthesis of Evidence (CHASE), Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.; The Graduate Group in Applied Mathematics and Computational Science, School of Arts and Sciences, University of Pennsylvania, Philadelphia, Pennsylvania, USA., Reps JM; Janssen Research and Development, Titusville, New Jersey, USA.; Observational Health Data Sciences and Informatics (OHDSI), New York, New York, USA.; Department of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands., Lorman V; Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA., Jing N; Biostatistics and Research Decision Sciences, Merck & Co., Inc, Rahway, New Jersey, USA., Edmondson M; Biostatistics and Research Decision Sciences, Merck & Co., Inc, Rahway, New Jersey, USA., Lou X; Health Outcomes & Biomedical informatics, College of Medicine, University of Florida, Gainesville, Florida, USA., Jhaveri R; Division of Infectious Diseases, Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, Illinois, USA., Kelleher KJ; Center for Child Health Equity and Outcomes Research, The Abigail Wexner Research Institute at Nationwide Children's Hospital, Columbus, Ohio, USA., Pajor NM; Divisions of Pulmonary Medicine | Biomedical Informatics | James M. Anderson Center for Health Systems Excellence, Cincinnati Children's Hospital Medical Center and University of Cincinnati College of Medicine, Cincinnati, Ohio, USA., Forrest CB; Applied Clinical Research Center, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA., Bian J; Health Outcomes & Biomedical informatics, College of Medicine, University of Florida, Gainesville, Florida, USA., Chu H; Statistical Research and Data Science Center, Pfizer Inc., New York, New York, USA., Chen Y; Center for Health AI and Synthesis of Evidence (CHASE), Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.; Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.; Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA.; Penn Institute for Biomedical Informatics (IBI), Philadelphia, Pennsylvania, USA.; Leonard Davis Institute of Health Economics, Philadelphia, Pennsylvania, USA.; Penn Medicine Center for Evidence-based Practice (CEP), Philadelphia, Pennsylvania, USA.
Jazyk: angličtina
Zdroj: Statistics in medicine [Stat Med] 2024 Dec 20; Vol. 43 (29), pp. 5573-5582. Date of Electronic Publication: 2024 Nov 03.
DOI: 10.1002/sim.10250
Abstrakt: 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.
(© 2024 The Author(s). Statistics in Medicine published by John Wiley & Sons Ltd.)
Databáze: MEDLINE
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