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Wiley, Biometrics, 3(73), p. 938-948, 2017

DOI: 10.1111/biom.12645

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A penalized framework for distributed lag non-linear models.

Journal article published in 2016 by Antonio Gasparrini, Fabian Scheipl, Ben Armstrong, Mg Kenward
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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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.