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Survival and Event History Analysis

Part of the Statistics for Biology and Health Book Series

DOI: 10.1007/978-0-387-68560-1

ISBN: 978-0-387-20287-7

Publisher: Springer New York - Springer Journals

Jan 1, 2008

1 - 10 of 11 Chapters

LitStream

Chapter 1An introduction to survival and event history analysis

Pages: 1 –39

[This book is about survival and event history analysis. This is a statistical methodology used in many different settings where one is interested in the occurrence of events. By events we mean occurrences in the lives of individuals that are of interest in scientific studies in medicine,...
LitStream

Chapter 2Stochastic processes in event history analysis

Pages: 41 –67

[Event histories unfold in time. Therefore, one would expect that tools from the theory of stochastic processes would be of considerable use in event history analysis. This is indeed the case, and in the present chapter we will review some basic concepts and results for stochastic processes that...
LitStream

Chapter 3Nonparametric analysis of survival and event history data

Pages: 69 –130

[In this chapter we study situations in which the data may be summarized by a counting process registering the occurrences of a specific event of interest or a few counting processes registering the occurrences of a few such events. One important example is the survival data situation, where one...
LitStream

Chapter 4Regression models

Pages: 131 –205

[In the previous chapter we considered situations where the data may be summarized into one or a few counting processes registering the occurrences of an event of interest. Such situations occur when the population in question is grouped into a few subpopulations according to the value of one or...
LitStream

Chapter 5Parametric counting process models

Pages: 207 –229

[In biostatistics it has become a tradition to use non- and semiparametric methods, like those considered in the previous two chapters, to analyze censored survival data, while parametric methods are more common in reliability studies of failure times of technical equipment. In our opinion...
LitStream

Chapter 6Unobserved heterogeneity: The odd effects of frailty

Pages: 231 –270

[Individuals differ. This is a basic observation of life and also of statistics. Some die old and some die young. Some are tall and some are small. In medicine one will find that a treatment that is useful for one person may be much less so for another person. And one person’s risk factor may be...
LitStream

Chapter 7Multivariate frailty models

Pages: 271 –300

[Data in survival analysis are usually assumed to be univariate, with one, possibly censored, lifetime for each individual. All the standard methodology, including Kaplan-Meier plots and Cox analysis, is geared toward handling this situation. However, multivariate survival data also arise...
LitStream

Chapter 8Marginal and dynamic models for recurrent events and clustered survival data

Pages: 301 –346

[We shall consider observation of clustered survival data or processes with recurrent events as defined in the introduction to Chapter 7. In the case of recurrent events we focus on the concept of dynamic models, which represent an attempt to understand in explicit terms how the past influences...
LitStream

Chapter 9Causality

Pages: 347 –385

[One of the exciting new developments in the field of statistics is a renewal of interest in the causality concept. This has led to several new approaches to defining and studying causality in statistical terms. Causality is based on a notion of the past influencing the present and the future....
LitStream

Chapter 10First passage time models: Understanding the shape of the hazard rate

Pages: 387 –424

[In Chapter 6 we saw how the individual variability in hazard can be described in terms of a random frailty variable. For instance, frailty may enter the hazard in a multiplicative fashion, with the individual hazard h described as h(t) = Zα(t). Here, α is a “basic” rate and Z is a nonnegative...

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