The authors define a class of “partially linear single‐index” survival models that are more
flexible than the classical proportional hazards regression models in their treatment of …

In survival analysis with competing risks, the transformation model allows different functions
between the outcome and explanatory variables. However, the model's prediction accuracy …

Survival distributions can be characterized by and compared through their hazard functions.
Tests using a proportional hazards model have good power if the two hazards do not cross …

Lifetime risk measures the cumulative risk for developing a disease over one's lifespan.
Modeling the lifetime risk must account for left truncation, the competing risk of death, and …

We give an overview of eight different software packages and functions available in R for
semi-or non-parametric estimation of the hazard rate for right-censored survival data. Of …

Modelling of censored survival data is almost always done by Cox proportional‐hazards
regression. However, use of parametric models for such data may have some advantages …

We discuss the impact of misspecifying fully parametric proportional hazards and
accelerated life models. For the uncensored case, misspecified accelerated life models give …

Background Prognostic models incorporating survival analysis predict the risk (ie,
probability) of experiencing a future event over a specific time period. In 2002, Royston and …

The Cox model is the most widely used survival model in the health sciences, but it is not the
only model available. In this chapter we present a class of survival models, called parametric …

Survival analysis deals with the analysis of times to events, or lifetimes. Parametric models
are used to represent the distributions of lifetimes, and their relationship to explanatory …