Details zur Publikation

Kategorie Textpublikation
Referenztyp Zeitschriften
DOI 10.1093/ije/dyw145
Titel (primär) Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis
Autor Dietrich, S.; Floegel, A.; Troll, M.; Kühn, T.; Rathmann, W.; Peters, A.; Sookthai, D.; von Bergen, M.; Kaaks, R.; Adamski, J.; Prehn, C.; Boeing, H.; Schulze, M.B.; Illig, T.; Pischon, T.; Knüppel, S.; Wang-Sattler, R.; Drogan, D.
Journal / Serie International Journal of Epidemiology
Erscheinungsjahr 2016
Department MOLSYB
Band/Volume 45
Heft 5
Seite von 1406
Seite bis 1420
Sprache englisch
Keywords Cox proportional hazards regression; exploratory survival analysis; multicollinearity; random survival forest; right-censored data; metabolomics; type 2 diabetes mellitus; variable selection
UFZ Querschnittsthemen RU3;

Background: The application of metabolomics in prospective cohort studies is statistically challenging. Given the importance of appropriate statistical methods for selection of disease-associated metabolites in highly correlated complex data, we combined random survival forest (RSF) with an automated backward elimination procedure that addresses such issues.

Methods: Our RSF approach was illustrated with data from the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam study, with concentrations of 127 serum metabolites as exposure variables and time to development of type 2 diabetes mellitus (T2D) as outcome variable. Out of this data set, Cox regression with a stepwise selection method was recently published. Replication of methodical comparison (RSF and Cox regression) was conducted in two independent cohorts. Finally, the R-code for implementing the metabolite selection procedure into the RSF-syntax is provided.

Results: The application of the RSF approach in EPIC-Potsdam resulted in the identification of 16 incident T2D-associated metabolites which slightly improved prediction of T2D when used in addition to traditional T2D risk factors and also when used together with classical biomarkers. The identified metabolites partly agreed with previous findings using Cox regression, though RSF selected a higher number of highly correlated metabolites.

Conclusions: The RSF method appeared to be a promising approach for identification of disease-associated variables in complex data with time to event as outcome. The demonstrated RSF approach provides comparable findings as the generally used Cox regression, but also addresses the problem of multicollinearity and is suitable for high-dimensional data.

dauerhafte UFZ-Verlinkung
Dietrich, S., Floegel, A., Troll, M., Kühn, T., Rathmann, W., Peters, A., Sookthai, D., von Bergen, M., Kaaks, R., Adamski, J., Prehn, C., Boeing, H., Schulze, M.B., Illig, T., Pischon, T., Knüppel, S., Wang-Sattler, R., Drogan, D. (2016):
Random Survival Forest in practice: a method for modelling complex metabolomics data in time to event analysis
Int. J. Epidemiol. 45 (5), 1406 - 1420