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Titel:

Application of regularized regression to identify novel predictors of mortality in a cohort of hemodialysis patients.

Dokumenttyp:
Article; Journal Article; Research Support, Non-U.S. Gov't
Autor(en):
Werfel, Stanislas; Lorenz, Georg; Haller, Bernhard; Günthner, Roman; Matschkal, Julia; Braunisch, Matthias C; Schaller, Carolin; Gundel, Peter; Kemmner, Stephan; Hayek, Salim S; Nusshag, Christian; Reiser, Jochen; Moog, Philipp; Heemann, Uwe; Schmaderer, Christoph
Abstract:
Cohort studies often provide a large array of data on study participants. The techniques of statistical learning can allow an efficient way to analyze large datasets in order to uncover previously unknown, clinically relevant predictors of morbidity or mortality. We applied a combination of elastic net penalized Cox regression and stability selection with the aim of identifying novel predictors of mortality in a cohort of prevalent hemodialysis patients. In our analysis we included 475 patients...     »
Zeitschriftentitel:
Sci Rep
Jahr:
2021
Band / Volume:
11
Heft / Issue:
1
Volltext / DOI:
doi:10.1038/s41598-021-88655-0
PubMed:
http://view.ncbi.nlm.nih.gov/pubmed/33927289
Print-ISSN:
2045-2322
TUM Einrichtung:
Fachgebiet Nephrologie (Prof. Heemann); Institut für Medizinische Statistik und Epidemiologie
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