Open AccessCombining 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.