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BACKGROUND When predictive survival models are built from high-dimensional data, there are often additional covariates, such as clinical scores, that by all means have to be included into the final model. While there are several techniques for the fitting of sparse high-dimensional survival models by penalized parameter estimation, none allows for explicit(More)
Transcranial magnetic stimulation (TMS) provides an intriguing in vivo method to investigate motor cortex excitability in men. This offers new insights into the neurophysiological basis of neuropsychiatric diseases. Earlier TMS studies in patients with schizophrenia revealed inconsistent results, probably due to major confounding variables like state of(More)
The use of generalized additive models in statistical data analysis suffers from the restriction to few explanatory variables and the problems of selection of smoothing parameters. Generalized additive model boosting circumvents these problems by means of stagewise fitting of weak learners. A fitting procedure is derived which works for all simple(More)
The bootstrap is a tool that allows for efficient evaluation of prediction performance of statistical techniques without having to set aside data for validation. This is especially important for high-dimensional data, e.g., arising from microarrays, because there the number of observations is often limited. For avoiding overoptimism the statistical(More)
Statistical heterogeneity and small-study effects are 2 major issues affecting the validity of meta-analysis. In this article, we introduce the concept of a limit meta-analysis, which leads to shrunken, empirical Bayes estimates of study effects after allowing for small-study effects. This in turn leads to 3 model-based adjusted pooled treatment-effect(More)
BACKGROUND Low frequency repetitive transcranial magnetic stimulation (rTMS) has been proposed as an innovative treatment for chronic tinnitus. The aim of the present study was to elucidate the underlying mechanism and to evaluate the relationship between clinical outcome and changes in cortical excitability. We investigated ten patients with chronic(More)
The use of generalized additive models in statistical data analysis suffers from the restriction to few explanatory variables and the problems of selection of smoothing parameters. Generalized additive model boosting circumvents these problems by means of stagewise fitting of weak learners. A fitting procedure is derived which works for all simple(More)
BACKGROUND There are several techniques for fitting risk prediction models to high-dimensional data, arising from microarrays. However, the biological knowledge about relations between genes is only rarely taken into account. One recent approach incorporates pathway information, available, e.g., from the KEGG database, by augmenting the penalty term in(More)
Many statistical analysis tasks in areas such as bioin-formatics are computationally very intensive, while lots of them rely on embarrassingly parallel computations (Grama et al., 2003). Multiple computers or even multiple processor cores on standard desktop computers, which are widespread nowadays, can easily contribute to faster analyses. R itself does(More)
MOTIVATION For analyzing high-dimensional time-to-event data with competing risks, tailored modeling techniques are required that consider the event of interest and the competing events at the same time, while also dealing with censoring. For low-dimensional settings, proportional hazards models for the subdistribution hazard have been proposed, but an(More)