ehm-lab/2017_gasparrini_Biomet_Rcodedata

R code reproducing the results published in the article: Gasparrini A, Scheipl F, Armstrong B, and Kenward MG. A penalized framework for distributed lag non-linear models. Biometrics. 2017;73(3):938-948. DOI: 10.1111/biom.12645. PMID: 28134978. This methodological article describes the extension of distributed lag linear and non-linear models (DLMs and DLNMs) thorugh generalized additive models via penalized splines. The methodology is implemented by embedding functions in the R packages dlnm and mgcv. The code reproduces two examples of application in time series and survival analysis, respectively, and the results of the simulation study. The software implementation and the use of the functions available in the R package dlnm are described in detail in the vignette that accompanies the package (also available at the CRAN page)

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