Combining Effect Estimates Across Cohorts and Sufficient Adjustment Sets for Collaborative Research | Zendy

Ghassan B. Hamra | Zendy; Catherine R. Lesko | Zendy; Jessie P. Buckley | Zendy; Elizabeth T. Jensen | Zendy; Daniel J. Tancredi | Zendy; Bryan Lau | Zendy; Irva HertzPicciotto | Zendy

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Combining Effect Estimates Across Cohorts and Sufficient Adjustment Sets for Collaborative Research

Author(s) -

Ghassan B. Hamra,

Catherine R. Lesko,

Jessie P. Buckley,

Elizabeth T. Jensen,

Daniel J. Tancredi,

Bryan Lau,

Irva HertzPicciotto

Publication year - 2021

Publication title -

epidemiology

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 1.901

H-Index - 173

eISSN - 1531-5487

pISSN - 1044-3983

DOI - 10.1097/ede.0000000000001336

Subject(s) - covariate , estimator , statistics , inverse probability weighting , directed acyclic graph , weighting , logistic regression , econometrics , confounding , contrast (vision) , random effects model , mathematics , computer science , meta analysis , medicine , artificial intelligence , algorithm , radiology

Collaborative research often combines findings across multiple, independent studies via meta-analysis. Ideally, all study estimates that contribute to the meta-analysis will be equally unbiased. Many meta-analyses require all studies to measure the same covariates. We explored whether differing minimally sufficient sets of confounders identified by a directed acyclic graph (DAG) ensures comparability of individual study estimates. Our analysis applied four statistical estimators to multiple minimally sufficient adjustment sets identified in a single DAG.

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