RGP is an
R package for analyzing healthcare claims data and
simulated data using penalized regression and machine learning methods.
This package contains function wrappers to create a simulated cohort,
group predictors based on functional targets (from KEGG and TTD) and
conventional groups (ATC/ICD systems) and analyze the data using various types
of penalized regression (LASSO) and machine learning methods (random forests and
cohort simulation (
functional target-based grouping - KEGG (
functional target-based grouping - TTD (
penalized regression, group-based analysis and results assessment
classification measures (
ttd_clean_data: data curated from TTD (drug-target, disease-target.. etc.) as binary matrices
Paper13_events.csv: file containing ICD list of the ADEs for the project
R: functions and packages used in the analysis
algorithms-task13.R: analysis methods wrappers functions
helpers.R: helper functions
ftsim: Functional Target Simulation; a full independent package to download, clean and manage TTD and KEGG data. It also use them to create a simple simulated cohort with grouped covariates.
README.md: We are here. We explain the project contents, used packages, and of course the directory structure.
See the documentation
? for more info.
We gratefully acknowledge the financial support from the innovation fund (“Innovationsfonds”) of the Federal Joint Committee in Germany (grant number: 01VSF16020).
Mariam R. Rizkallah\ Leibniz Institute for Prevention Research & Epidemiology - BIPS GmbH E-mail: rizkallah-issak [at] leibniz-bips [dot] de
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