Gasparrini, A. 2017. R code for: "Modeling exposure–lag–response associations with distributed lag non-linear models". [Online]. Github. Available from: https://github.com/gasparrini/2014_gasparrini_StatMed_Rcodedata
Gasparrini, A. R code for: "Modeling exposure–lag–response associations with distributed lag non-linear models" [Internet]. Github; 2017. Available from: https://github.com/gasparrini/2014_gasparrini_StatMed_Rcodedata
Gasparrini, A (2017). R code for: "Modeling exposure–lag–response associations with distributed lag non-linear models". [Data Collection]. Github. https://github.com/gasparrini/2014_gasparrini_StatMed_Rcodedata
Description
An example illustrating the extension of DLNMs for modelling exposure-lag-response associations beyond time series analysis. The code completely reproduces the examples and simulation study described in the associated article. This is complemented by the vignette dlnmExtended included in the R package dlnm, showing applications in alternative settings. The code consists of: [1] uminers.csv stores the data from the Colorado Plateau uranium miners cohort, including individual information for 3,347 male subjects; [2] the numbered files from 00.prep.R to 08.simres.R reproduce the results of the illustrative example and of the simulation study; [3] example.R offers a simple example with of fitting some models and displaying/summarizing the results
functions.R creates some functions used in the other scripts; [4] simprep.R and simrun.R are called from the other scripts for preparing and running the simulations
Keywords
Data capture method | Other |
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Date (Date published in a 3rd party system) | 15 January 2017 |
Language(s) of written materials | English |
Data Creators | Gasparrini, A |
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LSHTM Faculty/Department | Faculty of Public Health and Policy > Dept of Public Health, Environments and Society |
Participating Institutions | London School of Hygiene & Tropical Medicine, London, United Kingdom |
Date Deposited | 13 Dec 2018 14:36 |
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Last Modified | 08 Jul 2021 12:48 |
Publisher | Github |