Evidence map›Paper›PMID 35590267›Full record

SynthesisBMC medical research methodology2022

Meta-analysis under imbalance in measurement of confounders in cohort studies using only summary-level data.

Debashree Ray, Alvaro Muñoz, Mingyu Zhang, Xiuhong Li, Nilanjan Chatterjee, Lisa P Jacobson, Bryan Lau

Abstract readMeta-Analysis
PubMed Full text at PMC Publisher
In one paragraph

Synthesis in BMC medical research methodology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

4 citing papers in PubMed.

  1. Article
  2. A Review of Methods for Research Synthesis.Statistics in medicine · 2025
    Article
  3. Article
  4. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Debashree RayDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA. dray@jhu.edu.
Alvaro MuñozDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Mingyu ZhangDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Xiuhong LiDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Nilanjan ChatterjeeDepartment of Biostatistics, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Lisa P JacobsonDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA.
Bryan LauDepartment of Epidemiology, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, Maryland, USA. blau1@jhu.edu.

Funding

ECHODAC (Environmental Influences on Child Health Outcomes Data Analysis Center)U24OD023382 · OD · JOHNS HOPKINS UNIVERSITY · PI Diane J Catellier, LISA P JACOBSON · 2016 to 2026
$148.4M
Vaccine Response and Immunotherapeutics SWGP30AI094189 · NIAID · JOHNS HOPKINS UNIVERSITY · PI Richard E. Chaisson · 2012 to 2026
$67.0M
Alcohol Research Consortium in HIV: Relapse Prevention ArmP01AA029544 · NIAAA · JOHNS HOPKINS UNIVERSITY · PI CHANDER, GEETANJALI, MCCAUL, MARY E · 2021 to 2025
$7.1M
3/4 Alcohol Research Consortium in HIV - Epidemiological Research Arm (ARCH-ERA)U01AA020793 · NIAAA · UNIVERSITY OF WASHINGTON · PI CRANE, HEIDI M., LAU, BRYAN · 2011 to 2020
$5.8M
Medicaid data as a complement to cohort studies for investigating cancers among older people with HIVR01CA250851 · NCI · JOHNS HOPKINS UNIVERSITY · PI Corinne E. Joshu, Bryan Lau · 2020 to 2026
$4.8M
At the Intersection of HIV and COVID-19: Medicaid Data as a Complement to Cohort StudiesR01AI170240 · NIAID · JOHNS HOPKINS UNIVERSITY · PI JOSHU, CORINNE E., LAU, BRYAN · 2022 to 2025
$3.8M
NCI NIH HHS R01 CA250851NIAAA NIH HHS P01 AA029544NIAAA NIH HHS U01 AA020793NIAID NIH HHS P30 AI094189NIAID NIH HHS R01 AI170240NIH HHS U24 OD023382
6 · The paper itself

Abstract

backgroundCohort collaborations often require meta-analysis of exposure-outcome association estimates across cohorts as an alternative to pooling individual-level data that requires a laborious process of data harmonization on individual-level data. However, it is likely that important confounders are not all measured uniformly across the cohorts due to differences in study protocols. This imbalance in measurement of confounders leads to association estimates that are not comparable across cohorts and impedes the meta-analysis of results.

methodsIn this article, we empirically show some asymptotic relations between fully adjusted and unadjusted exposure-outcome effect estimates, and provide theoretical justification for the same. We leverage these results to obtain fully adjusted estimates for the cohorts with no information on confounders by borrowing information from cohorts with complete measurement on confounders. We implement this novel method in CIMBAL (confounder imbalance), which additionally provides a meta-analyzed estimate that appropriately accounts for the dependence between estimates arising due to borrowing of information across cohorts. We perform extensive simulation experiments to study CIMBAL's statistical properties. We illustrate CIMBAL using National Children's Study (NCS) data to estimate association of maternal education and low birth weight in infants, adjusting for maternal age at delivery, race/ethnicity, marital status, and income.

resultsOur simulation studies indicate that estimates of exposure-outcome association from CIMBAL are closer to the truth than those from commonly-used approaches for meta-analyzing cohorts with disparate confounder measurements. CIMBAL is not too sensitive to heterogeneity in underlying joint distributions of exposure, outcome and confounders but is very sensitive to heterogeneity of confounding bias across cohorts. Application of CIMBAL to NCS data for a proof-of-concept analysis further illustrates the utility and advantages of CIMBAL.

conclusionsCIMBAL provides a practical approach for meta-analyzing cohorts with imbalance in measurement of confounders under a weak assumption that the cohorts are independently sampled from populations with the same confounding bias.

Indexed as

Research DesignBiasChildCohort StudiesComputer SimulationHumansInfantBiasCollective analysisConfounder imbalanceData integrationMeta-analysisOmitted covariateOmitted variable biasRegression estimates

Identifiers

PMID35590267
PMCPMC9118777

What OpenQuestion holds

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Registered trials

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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.