ArticleEpidemiology (Cambridge, Mass.)2024
Overcoming Data Gaps in Life Course Epidemiology by Matching Across Cohorts.
Article in Epidemiology (Cambridge, Mass.), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Combining Observational Studies to Reduce Multiple Biases.Epidemiology (Cambridge, Mass.) · 2026Article
- Income Volatility During Early to Mid-adulthood and 10-year Memory Decline in a Longitudinal Synthetic Cohort.Epidemiology (Cambridge, Mass.) · 2026Article
- Empowering multi-source cohort data integration and sharing in the China Cohort Consortium.The Lancet regional health. Western Pacific · 2026Article
- Associations of alcohol use in early and middle adulthood with mid- and late-life cognition - a synthetic cohort approach.medRxiv : the preprint server for health sciences · 2026Article
- Obesity from Childhood to Mid-adulthood in the United States: A Synthetic Cohort Approach to Measuring Health Trajectories.Epidemiology (Cambridge, Mass.) · 2026Article
- An Introduction to Longitudinal Synthetic Cohorts for Studying the Life Course Drivers of Health Outcomes and Inequalities in Older Age.Current epidemiology reports · 2025Article
- Key research priorities in methodological approaches for measuring the exposome and studying its role in the development of dementia.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2025Review
- Health economic evaluation of trans-tibial prosthetic suspension systems: a protocol for a pilot using an observational study and synthetic cohort.Cost effectiveness and resource allocation : C/E · 2025Article
- Semantic search helper: A tool based on the use of embeddings in multi-item questionnaires as a harmonization opportunity for merging large datasets - A feasibility study.European psychiatry : the journal of the Association of European Psychiatrists · 2025Article
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7 authors.
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Abstract
Life course epidemiology is hampered by the absence of large studies with exposures and outcomes measured at different life stages in the same individuals. We describe when the effect of an exposure ( A ) on an outcome ( Y ) in a target population is identifiable in a combined ("synthetic") cohort created by pooling an early-life cohort including measures of A with a late-life cohort including measures of Y . We enumerate causal assumptions needed for unbiased effect estimation in the synthetic cohort and illustrate by simulating target populations under four causal models. From each target population, we randomly sampled early- and late-life cohorts and created a synthetic cohort by matching individuals from the two cohorts based on mediators and confounders. We estimated the effect of A on Y in the synthetic cohort, varying matching variables, the match ratio, and the strength of association between matching variables and A . Finally, we compared bias in the synthetic cohort estimates when matching variables did not d-separate A and Y to the bias expected in the original cohort. When the set of matching variables includes all variables d-connecting exposure and outcome (i.e., variables blocking all backdoor and front-door pathways), the synthetic cohort yields unbiased effect estimates. Even when matching variables did not fully account for confounders, the synthetic cohort estimate was sometimes less biased than comparable estimates in the original cohort. Methods based on merging cohorts may hasten the evaluation of early- and mid-life determinants of late-life health but rely on available measures of both confounders and mediators.
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