Overcoming data gaps in life course epidemiology by matching across cohorts
- PMID: 38967975
- DOI: 10.1097/EDE.0000000000001761
Overcoming data gaps in life course epidemiology by matching across cohorts
Abstract
Lifecourse epidemiology is hampered by the absence of large studies with exposures and outcomes measured at different life stages on 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 back- 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.
Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.
Conflict of interest statement
Conflicts of Interest: None declared
Similar articles
-
Mortality and Morbidity Effects of Long-Term Exposure to Low-Level PM2.5, BC, NO2, and O3: An Analysis of European Cohorts in the ELAPSE Project.Res Rep Health Eff Inst. 2021 Sep;2021(208):1-127. Res Rep Health Eff Inst. 2021. PMID: 36106702 Free PMC article.
-
Folic acid supplementation and malaria susceptibility and severity among people taking antifolate antimalarial drugs in endemic areas.Cochrane Database Syst Rev. 2022 Feb 1;2(2022):CD014217. doi: 10.1002/14651858.CD014217. Cochrane Database Syst Rev. 2022. PMID: 36321557 Free PMC article.
-
Meta-analysis under imbalance in measurement of confounders in cohort studies using only summary-level data.BMC Med Res Methodol. 2022 May 19;22(1):143. doi: 10.1186/s12874-022-01614-9. BMC Med Res Methodol. 2022. PMID: 35590267 Free PMC article.
-
Digital Breast Tomosynthesis with Hologic 3D Mammography Selenia Dimensions System for Use in Breast Cancer Screening: A Single Technology Assessment [Internet].Oslo, Norway: Knowledge Centre for the Health Services at The Norwegian Institute of Public Health (NIPH); 2017 Sep 4. Report from the Norwegian Institute of Public Health No. 2017-08. Oslo, Norway: Knowledge Centre for the Health Services at The Norwegian Institute of Public Health (NIPH); 2017 Sep 4. Report from the Norwegian Institute of Public Health No. 2017-08. PMID: 29553669 Free Books & Documents. Review.
-
Assessing Adverse Health Effects of Long-Term Exposure to Low Levels of Ambient Air Pollution: Implementation of Causal Inference Methods.Res Rep Health Eff Inst. 2022 Jan;2022(211):1-56. Res Rep Health Eff Inst. 2022. PMID: 36193708 Free PMC article. Review.
LinkOut - more resources
Full Text Sources
