gms | German Medical Science

One important topic of current research on observational and especially prognostic factor studies is the development of methods that can be employed to analyse high-dimensional data, where the number of explanatory variables is much larger than the number of observations. This is mainly driven by the requirements of biomedical applications such as DNA microarrays. The major problem of analyzing such data is the danger of overfitting.

Methodological challenges arise in using large sets of covariates, e.g. patients gene expression profiles, to predict survival endpoints on account of the large number of variables and their complex interdependence.

The aim of this talk is to show how penalized regression models can be employed to analyse high-dimensional data. This include linear, logistic and proportional hazards regression models.

We illustrate the different approaches using real data examples from clinical microarray studies including gene expression data. The results will be discussed with respect to the prediction error and interpretability of the results.