Modeling exposure–lag–response associations with distributed lag non-linear models
By Antonio Gasparrini. Statistics in Medicine, 2013
745 citations recorded by the source corpus.
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. © 2013 The Authors. Statistics in Medicine published by John Wiley & Sons, Ltd.
Subfields: General Health Professions, Health, Toxicology and Mutagenesis
View Paper: https://www.semanticscholar.org/paper/45709ef62b4f34fa5fd3da793d4104ef9ed47fd7
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In General Health Professions
In Health, Toxicology and Mutagenesis
Citations in the corpus
- A machine learning method to estimate PM2.5 concentrations across China with remote sensing, meteorological and land use information. (2018, methodology): “…(Chen et al., 2018), where cb_TEMP , cb_RH , cb_BP and cb_WS are mean temperature, relative humidity, barometric pressure and wind speed on the current day and previous two days (lag 0-2 days) fitted using crossbasis() function of DLNM with 3 df (Gasparrini, 2011; Gasparrini, 2014), respectively.”