In the era of precision medicine, time-to-event outcomes such as time to death or
progression are routinely collected, along with high-throughput covariates. These high …

With widespread availability of omics profiling techniques, the analysis and interpretation of
high-dimensional omics data, for example, for biomarkers, is becoming an increasingly …

In recent years, breakthroughs in biomedical technology have led to a wealth of data in
which the number of features (for instance, genes on which expression measurements are …

Background In clinical research, the primary interest is often the time until occurrence of an
adverse event, ie, survival analysis. Its application to electronic health records is challenging …

Motivation: Penalized regression methods have been adopted widely for high-dimensional
feature selection and prediction in many bioinformatic and biostatistical contexts. While their …

High‐dimensional data are becoming increasingly common in the medical field as large
volumes of patient information are collected and processed by high‐throughput screening …

Many different models for the analysis of high-dimensional survival data have been
developed over the past years. While some of the models and implementations come with …

Survival analysis aims to predict the occurrence of specific events of interest at future time
points. The presence of incomplete observations due to censoring brings unique challenges …

Background In the era of personalized medicine, it's primordial to identify gene signatures
for each event type in the context of competing risks in order to improve risk stratification and …

Translating the vast data generated by genomic platforms into accurate predictions of
clinical outcomes is a fundamental challenge in genomic medicine. Many prediction …