Distributed lag non-linear models

By Antonio Gasparrini, Ben Armstrong, Michael G. Kenward. Statistics in Medicine, 2010

2,126 citations recorded by the source corpus.

Environmental stressors often show effects that are delayed in time, requiring the use of statistical models that are flexible enough to describe the additional time dimension of the exposure–response relationship. Here we develop the family of distributed lag non‐linear models (DLNM), a modelling framework that can simultaneously represent non‐linear exposure–response dependencies and delayed effects. This methodology is based on the definition of a ‘cross‐basis’, a bi‐dimensional space of functions that describes simultaneously the shape of the relationship along both the space of the predictor and the lag dimension of its occurrence. In this way the approach provides a unified framework for a range of models that have previously been used in this setting, and new more flexible variants. This family of models is implemented in the package dlnm within the statistical environment R. To illustrate the methodology we use examples of DLNMs to represent the relationship between temperature and mortality, using data from the National Morbidity, Mortality, and Air Pollution Study (NMMAPS) for New York during the period 1987–2000. Copyright © 2010 John Wiley & Sons, Ltd.

Subfields: Environmental Engineering, Health, Toxicology and Mutagenesis

View Paper: https://www.semanticscholar.org/paper/68034e812be7c0994de0238fa6d22b79e301169f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Environmental Engineering

1. Antonio Gasparrini
2. M. Leone
3. Ben Armstrong

In Health, Toxicology and Mutagenesis

1. Antonio Gasparrini
2. M. Leone
3. Ben Armstrong

Citations in the corpus

• Temperature (2019, neutral): “The mesoscopic thermodynamics with 1-particle distribution function as state variable, called kinetic theory, has been pioneered by Ludwig Boltzmann [2].”
• Reducing and meta-analysing estimates from distributed lag non-linear models (2013, methodology): “Details on the algebraic development are given elsewhere [5] [sections 4.”
• Distributed Lag Linear and Non-Linear Models in R: The Package dlnm (2011, methodology): “This step has lead to the generation of the new modeling framework of distributed lag non-linear models (DLNMs) (Gasparrini et al. 2010; Armstrong 2006), implemented in the R package dlnm (Gasparrini and Armstrong 2010).”
• Modeling exposure–lag–response associations with distributed lag non-linear models (2013, neutral): “The algebraic representation has been previously presented [24], and a revised version is proposed here.”
• Attributable risk from distributed lag models (2014, methodology): “In time series analysis, a popular approach is based on distributed lag models (DLMs) [7,8], generalized to distributed lag non-linear models (DLNMs) when including non-linear exposure-response associations [9,10].”