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 …

This volume examines modern techniques and research problems in the analysis of lifetime
data analysis. This area of statistics deals with time-to-event data which is complicated not …

A new method called SurvLIME for explaining machine learning survival models is
proposed. It can be viewed as an extension or modification of the well-known method LIME …

Motivation Identification of genomic, molecular and clinical markers prognostic of patient
survival is important for developing personalized disease prevention, diagnostic and …

Objectives The objective of this study was to build and assess the performance of survival
prediction models using the gender-specific informative risk factors for patients with left …

A weighted random survival forest is presented in the paper. It can be regarded as a
modification of the random forest improving its performance. The main idea underlying the …

A new robust algorithm based on the explanation method SurvLIME called SurvLIME-KS is
proposed for explaining machine learning survival models. The algorithm is developed to …

A method for counterfactual explanation of machine learning survival models is proposed.
One of the difficulties of solving the counterfactual explanation problem is that the classes of …

An extension of the Neural Additive Model (NAM) called SurvNAM and its modifications are
proposed to explain predictions of a black-box machine learning survival model. The …

In this article, we propose a variable selection approach in the Cox model when there is a
group structure in a diverging number of covariates. Most of the existing variable selection …