Publication:
Proceedings of the 33rd International workshop on Statistical Modelling;vol, 1, pp. 237-241
Publication type:
Conference Object
Abstract:
The Weibull multi-parameter regression (MPR) model with frailty
is developed for interval censored survival data. The basic MPR model which is
wholly parametric with non-proportional hazards was developed by Burke and
MacKenzie in their 2016 Biometrics paper. We describe the basic model, develop
the interval-censored likelihood and extend the model to include Gamma frailty.
We present a simulation study and re-analyse data from the Signal Tandmo-
biel study. The MPR model is shown to be superior to a proportional hazards
competitor.
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 ...
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 ...
We develop an analysis of the reliability of reliability data following
a negative exponential distribution and subject to type I censoring. Our main
focus is to determine the role of the particular analytical approach ...
In the parametric regression models with categorical covariates, it is well
known that many key quantities of interest are invariant to the choice
of reference subclass. However, surprisingly, not all quantities are ...
We aim to explore the survival distributions based on the extreme
dispersion, XD, models proposed by Jørgensen (2010). It is suggested that survival times can be modelled within the XD framework by taking log T = Y ~ ...
