Over the last five decades, we have seen strong methodological advances in survival
analysis, mainly in two separate strands: One strand is based on a parametric approach that …

Abstract flexsurv is an R package for fully-parametric modeling of survival data. Any
parametric time-to-event distribution may be fitted if the user supplies a probability density or …

This book, authored by Prof. Catherine Legrand, is not only valuable but also a timely
addition to the existing vast collection of books on survival analysis. It provides a …

The hazard function represents one of the main quantities of interest in the analysis of
survival data. We propose a general approach for modelling the dynamics of the hazard …

Survival analysis is an important field of Statistics concerned with mak-ing time-to-event
predictions with 'censored'data. Machine learning, specifically supervised learning, is the …

The conditional survival function of a time-to-event outcome subject to censoring and
truncation is a common target of estimation in survival analysis. This parameter may be of …

Parametric survival models are being increasingly used as an alternative to the Cox model
in biomedical research. Through direct modelling of the baseline hazard function, we can …

Proportional hazards (PH), proportional odds (PO) and accelerated failure time (AFT)
models have been widely used to deal with survival data in different fields of knowledge …

In this article, we describe methodology that allows for multiple timescales using flexible
parametric survival models without the need for time splitting. When one fits flexible …

Survival analysis problems have elsewhere been recast as problems in logistic regression,
after the event times were grouped into intervals. Here we discuss the opposite connection …