Modeling exposure–lag–response associations with distributed lag non‐linear models

2013/09/12 by Antonio Gasparrini · 17 citations
Environmental Science · Health Professions · #Air Quality and Health Impacts #Climate Change and Health Impacts #Global Health Care Issues

paper · pdf · doi:10.1002/sim.5963

openalex publication_date 2013/09/12 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/31

Abstract

In biomedical research, a health effect is frequently associated with protracted exposures of varying intensity sustained in the past. The main complexity of modeling and interpreting such phenomena lies in the additional temporal dimension needed to express the association, as the risk depends on both intensity and timing of past exposures. This type of dependency is defined here as exposure-lag-response association. In this contribution, I illustrate a general statistical framework for such associations, established through the extension of distributed lag non-linear models, originally developed in time series analysis. This modeling class is based on the definition of a cross-basis, obtained by the combination of two functions to flexibly model linear or nonlinear exposure-responses and the lag structure of the relationship, respectively. The methodology is illustrated with an example application to cohort data and validated through a simulation study. This modeling framework generalizes to various study designs and regression models, and can be applied to study the health effects of protracted exposures to environmental factors, drugs or carcinogenic agents, among others.

Citations

• Distributed lag non‐linear models

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