ArticleEnvironmental health perspectives2024
Using Parametric g-Computation for Time-to-Event Data and Distributed Lag Models to Identify Critical Exposure Windows for Preterm Birth: An Illustrative Example Using
Article in Environmental health perspectives, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Clarifying causal and translational inference in studies of ambient PM2.5 and lung cancer.British journal of cancer · 2026Article
- Gestational misalignment with fixed exposure windows: the potential dangers of zero-filling in distributed lag models.International journal of epidemiology · 2026Article
- Article
- Associations between prenatal exposure to ambient air pollutants and preterm birth in the Atlanta African American maternal-child cohort.Environmental health : a global access science source · 2026Article
- Prenatal Harmattan exposure and birth size: identifying sensitive windows in pregnancy.Frontiers in global women's health · 2026Article
- A hypothetical intervention analysis for the effects of healthy dietary patterns on reducing major chronic diseases and mortality associated with air pollutant mixtures.BMC medicine · 2025Article
- Impact of Heat on Birth Outcomes and Child Nutrition: Study Protocol using the CIDACS Birth Cohort.Wellcome open research · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
backgroundParametric g-computation is an attractive analytic framework to study the health effects of air pollution. Yet, the ability to explore biologically relevant exposure windows within this framework is underdeveloped.
objectivesWe outline a novel framework for how to incorporate complex lag-responses using distributed lag models (DLMs) into parametric g-computation analyses for survival data. We call this approach "g-survival-DLM" and illustrate its use examining the association between
methodsWe applied the g-survival-DLM approach to estimate the hypothetical static intervention of reducing average
resultsThere were 762 (8.1%) PTBs in this cohort. The gestational week-specific median DISCUSSION: We demonstrate that our g-survival-DLM approach produces easier-to-interpret, policy-relevant estimates (due to the g-computation); prevents immortal time bias (due to treating PTB as a time-to-event outcome); and allows for the exploration of critical exposure windows (due to the DLMs). In our illustrative example, we found that reducing fine particulate matter [particulate matter (PM) with aerodynamic diameter
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