Tags: Statistics
Modeling Survival Data Using Frailty Models 1st Edition
TITLE : Modeling Survival Data Using
Frailty Models 1st Edition
ISBN : 9781439836675
AUTHOR : David D. Hanagal (Author)
PUBLISHER : Taylor
FORMAT: Hardcover
PAGES : 334
YEAR PUBLICATIONS : 2011
LANGUAGE: English
SUBJECT: Statistics
WEIGHT (KG): 0.6
CONDITION: Used - Very Good
DESCRIPTION:
"When designing and analyzing
a medical study, researchers focusing on survival data must take into account
the heterogeneity of the study population: due to uncontrollable variation,
some members change states more rapidly than others. Survival data measures the
time to a certain event or change of state. For example, the event may be
death, occurrence of disease, time to an epileptic seizure, or time from
response until disease relapse. Frailty is a convenient method to introduce
unobserved proportionality factors that modify the hazard functions of an
individual.
In spite of several new research
developments on the topic, there are very few books devoted to frailty models.
Modeling Survival Data Using Frailty Models covers recent advances in
methodology and applications of frailty models, and presents survival analysis
and frailty models ranging from fundamental to advanced. Eight data on survival
times with covariates sets are discussed, and analysis is carried out using the
R statistical package.
This book covers:
·
Basic concepts in survival
analysis, shared frailty models and bivariate frailty models
·
Parametric distributions and their
corresponding regression models
·
Nonparametric Kaplan–Meier
estimation and Cox's proportional hazard model
·
The concept of frailty and
important frailty models
·
Different estimation procedures
such as EM and modified EM algorithms
·
Logrank tests and CUSUM of
chi-square tests for testing frailty
·
Shared frailty models in different
bivariate exponential and bivariate Weibull distributions
·
Frailty models based on Lévy
processes
·
Different estimation procedures in
bivariate frailty models
·
Correlated gamma frailty,
lognormal and power variance function frailty models
·
Additive frailty models
·
Identifiability of bivariate
frailty and correlated frailty models
The problem of analyzing time to event data arises in a number of
applied fields, such as medicine, biology, public health, epidemiology,
engineering, economics, and demography. Although the statistical tools
presented in this book are applicable to all these disciplines, this book
focuses on frailty in biological and medical statistics, and is designed to
prepare students and professionals for experimental design and analysis."
