Overcoming data gaps in life course epidemiology by matching across cohorts

Lifecourse epidemiology is hampered by the absence of studies with exposures and outcomes measured at different life stages. 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 exposure measures with a late-life cohort including outcome measures. We enumerate the causal assumptions needed for unbiased effect estimation in the synthetic cohort and illustrate by simulating target populations under four causal models. From each population, we drew hypothetical early- and late-life cohorts and created a synthetic cohort by matching individuals from the two cohorts based on mediators and/or confounders. We compared bias when estimating the effect of A on Y in the synthetic cohort, varying which matching variables were available, the match ratio, and the distance matching criterion. 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. Methods based on merging cohorts provide opportunities to hasten the evaluation of early- and mid-life determinants of late life health, but rely on available measures of both confounders and mediators. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement No external funding was received. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: the data is simulated and thus IRB approval is n/a I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes The data is simulated and thus data sharing is not applicable.