Publication:
Proceedings of the 22nd International Workshop on Statistical Modelling;
Publication type:
Conference Object
Abstract:
Kaplan-Meier analysis of a large breast cancer dataset is carried
out under all-cause and cause-specific survival. The results are compared with
a variety of model-based analyses, including Cox's Proportional Hazard (PH)
model and its Gamma frailty variant, along with the non-PH Generalised Time-
Dependent Logistic Model (GTDL) and its Gamma frailty variant.
We compare and contrast the properties of the bivariate Weibull
and GTDL regression survival models. An analytic expression for the correlation
between times is derived for the Weibull model and a modified Kullback-Lei ...
In the survival analysis literature, the standard model for data analysis is the
semi-parametric Proportional Hazard (PH) model of Cox (1972). MacKenzie
(1996) introduced the Generalised Time Dependent Logistic (GTDL) ...
The proportional hazards (PH) assumption in survival analysis may
not always be appropriate. If data do not obey the assumption then we will
reach incorrect conclusions by making it. For example we may find a covariate
to ...
Frailty models are now widely used for analyzing multivariate survival data. An open question is how best to determine how to select the most
appropriate frailty structure supported by the data. Herein, we develop a ...
We present a general likelihood for interval censored survival data
arising in longitudinal studies such as longitudinal randomized controlled clinical
trials (RCTs) and give some general formulae for inference in ...
