Generalizing Findings in Nested Cohorts.

Authors

Ashley I Naimi, Ya-Hui Yu, Sara M Parisi, Lisa M Bodnar

Published in

Epidemiology (Cambridge, Mass.). Volume 37. Issue 5. Pages 578-581. Sep 01, 2026. Epub May 25, 2026.

Abstract

Many research findings are based on a nested subset of an original cohort followed over time, but results are rarely generalized back to the original population. Here, we conduct a "proof of concept" analysis demonstrating the application of simple methods to generalize the association between prepregnancy obesity and preeclampsia in women from the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers to Be (nuMoM2b, target sample), using a subset of women whose follow-up was extended to 3 years postpartum in the Heart Health Study (nuMoM2b-HHS, source sample). We constructed inverse probability of selection weights and estimated three risk ratios for the association between obesity and preeclampsia: in the target nuMoM2b sample (N = 9,920), in the source nuMoM2b-HHS sample (N = 4,486), and in the weighted source nuMoM2b-HHS sample generalized back to the target sample using inverse probability of selection weights (pseudo N = 4,468). In the target, source, and weighted samples, the estimated risk ratios (95% confidence intervals) were 2.0 (1.7, 2.3), 2.2 (1.8, 2.6), and 2.0 (1.6, 2.4), respectively. We discuss the assumptions involved in generalizing study findings and methods available for doing so. When relevant, researchers should deploy methods for generalizing study nested cohort findings back to a target sample.

PMID:
42525474
Bibliographic data and abstract were imported from PubMed on 30 Jul 2026.

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