ArticleBiometrika2019
Generalized meta-analysis for multiple regression models across studies with disparate covariate information.
Article in Biometrika, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 4 of them syntheses that pooled it.
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Who cites it
28 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Systematically missing data in causally interpretable meta-analysis.Biostatistics (Oxford, England) · 2024Pooled it
- A meta-inference framework to integrate multiple external models into a current study.Biostatistics (Oxford, England) · 2023Pooled it
- Meta-analysis under imbalance in measurement of confounders in cohort studies using only summary-level data.BMC medical research methodology · 2022Pooled it
- Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations.PloS one · 2022Pooled it
- A new information integration framework for complex models with applications to real-world data.Statistical methods in medical research · 2026Article
- A Review of Methods for Research Synthesis.Statistics in medicine · 2025Article
- AmpC β-Lactamase-Producing Microorganisms in South American Hospitals: A Meta-Regression Analysis, Meta-Analysis, and Review of Prevalence.Tropical medicine and infectious disease · 2025Review
- Robust angle-based transfer learning in high dimensions.Journal of the Royal Statistical Society. Series B, Statistical methodology · 2025Article
- Expanding scope of genetic studies in the era of biobanks.Human molecular genetics · 2025Article
- Development and validation of a risk prediction model for premenopausal breast cancer in 19 cohorts.Breast cancer research : BCR · 2025Article
- A comparison of some existing and novel methods for integrating historical models to improve estimation of coefficients in logistic regression.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2025Article
- Gerontologic Biostatistics and Data Science: Aging Research in the Era of Big Data.The journals of gerontology. Series A, Biological sciences and medical sciences · 2024Review
- Improving prediction of linear regression models by integrating external information from heterogeneous populations: James-Stein estimators.Biometrics · 2024Article
- Integrating external summary information in the presence of prior probability shift: an application to assessing essential hypertension.Biometrics · 2024Article
- INTEGRATING INCOMPLETE DATA FOR MEDIATION ANALYSIS.Statistica Sinica · 2024Article
- Pulmonary Fibrosis Stakeholder Summit: A Joint NHLBI, Three Lakes Foundation, and Pulmonary Fibrosis Foundation Workshop Report.American journal of respiratory and critical care medicine · 2024Article
- Robust data integration from multiple external sources for generalized linear models with binary outcomes.Biometrics · 2024Article
- A synthetic data integration framework to leverage external summary-level information from heterogeneous populations.Biometrics · 2023Article
- The Environmental Influences on Child Health Outcomes (ECHO)-Wide Cohort.American journal of epidemiology · 2023Article
- Integrative analysis of individual-level data and high-dimensional summary statistics.Bioinformatics (Oxford, England) · 2023Article
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3 authors.
Funding
Abstract
Meta-analysis is widely popular for synthesizing information on common parameters of interest across multiple studies because of its logistical convenience and statistical efficiency. We develop a generalized meta-analysis approach to combining information on multivariate regression parameters across multiple studies that have varying levels of covariate information. Using algebraic relationships among regression parameters in different dimensions, we specify a set of moment equations for estimating parameters of a maximal model through information available from sets of parameter estimates for a series of reduced models from the different studies. The specification of the equations requires a reference dataset for estimating the joint distribution of the covariates. We propose to solve these equations using the generalized method of moments approach, with the optimal weighting of the equations taking into account uncertainty associated with estimates of the parameters of the reduced models. We describe extensions of the iterated reweighted least-squares algorithm for fitting generalized linear regression models using the proposed framework. Based on the same moment equations, we also develop a diagnostic test for detecting violations of underlying model assumptions, such as those arising from heterogeneity in the underlying study populations. The proposed methods are illustrated with extensive simulation studies and a real-data example involving the development of a breast cancer risk prediction model using disparate risk factor information from multiple studies.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.