A penalized framework for distributed lag non-linear models.


Gasparrini, A; Scheipl, F; Armstrong, B; Kenward, MG; (2017) A penalized framework for distributed lag non-linear models. Biometrics. ISSN 0006-341X DOI: 10.1111/biom.12645

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Abstract

: Distributed lag non-linear models (DLNMs) are a modelling tool for describing potentially non-linear and delayed dependencies. Here, we illustrate an extension of the DLNM framework through the use of penalized splines within generalized additive models (GAM). This extension offers built-in model selection procedures and the possibility of accommodating assumptions on the shape of the lag structure through specific penalties. In addition, this framework includes, as special cases, simpler models previously proposed for linear relationships (DLMs). Alternative versions of penalized DLNMs are compared with each other and with the standard unpenalized version in a simulation study. Results show that this penalized extension to the DLNM class provides greater flexibility and improved inferential properties. The framework exploits recent theoretical developments of GAMs and is implemented using efficient routines within freely available software. Real-data applications are illustrated through two reproducible examples in time series and survival analysis.<br/>

Item Type: Article
Faculty and Department: Faculty of Public Health and Policy > Dept of Social and Environmental Health Research
Faculty of Epidemiology and Population Health > Dept of Medical Statistics
PubMed ID: 28134978
URI: http://researchonline.lshtm.ac.uk/id/eprint/3429699

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