5 papers
Selection instability and coefficient uncertainty in Cox modelling after Lasso selection
Lena Schemet, Andreas Groll, Sarah Friedrich-Welz
After variable selection, uncertainty quantification often treats the selected model as fixed, although it may change under repeated sampling. We investigate whether this variabili…
Wild Bootstrap and Efron's Bootstrap for Debiased Cox Regression
Lena Schemet, Sarah Friedrich-Welz
Cox regression with Lasso penalization is widely used for variable selection in time-to-event data, but reliable coefficient inference after selection remains difficult. We investi…
Model-based bootstrap inference for Cox models after Lasso selection
Lena Schemet, Andreas Groll, Sarah Friedrich-Welz
Inference after variable selection in Cox regression is difficult because simple Wald-type intervals after selection can have poor finite-sample conditional coverage. We study a mo…
A reproducible and extensible framework for benchmarking competing risks survival models
Begoña B. Sierra, Colin McLean, Peter S. Hall +2
A wide range of statistical and machine learning methods have been proposed for survival analysis with competing risks, where the occurrence of one event (i.e., cancer death) precl…
Statistical inference after variable selection in Cox models: A simulation study
Lena Schemet, Sarah Friedrich-Welz
Choosing relevant predictors is central to the analysis of biomedical time-to-event data. Classical frequentist inference, however, presumes that the set of covariates is fixed in…