Distributed lag non-linear models
Article first published online: 7 MAY 2010
Copyright © 2010 John Wiley & Sons, Ltd.
Statistics in Medicine
Volume 29, Issue 21, pages 2224–2234, 20 September 2010
How to Cite
Gasparrini, A., Armstrong, B. and Kenward, M. G. (2010), Distributed lag non-linear models. Statist. Med., 29: 2224–2234. doi: 10.1002/sim.3940
- Issue published online: 26 AUG 2010
- Article first published online: 7 MAY 2010
- Manuscript Accepted: 18 MAR 2010
- Manuscript Received: 4 NOV 2009
- Re-use of this article is permitted in accordance with the Terms and Conditions set out at [http://wileyonlinelibrary.com/author resources/onlineopen.html]
- distributed lag;
- time series;
- delayed effects
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.