The place in survival analysis now occupied by proportional hazards models and their generalizations is so large that it is no longer conceivable to offer a course on the subject without devoting at least half of the content to this topic alone. This book focuses on the theory and applications of a very broad class of models - proportional hazards and non-proportional hazards models, the former being viewed as a special case of the latter - which, underlie modern survival analysis.
Unlike other books in this area the emphasis is not on measure theoretic arguments for stochastic integrals and martingales. Instead, while inference based on counting processes and the theory of martingales is covered, much greater weight is placed on more traditional results such as the functional central limit theorem. This change in emphasis allows us in the book to devote much greater consideration to practical issues in modeling. The implications of different models, their practical interpretation, the predictive ability of any model, model construction, and model selection as well as the whole area of miss-specified models receive a great deal of attention.
The book is aimed at both those interested in theory and those interested in applications. Many examples and illustrations are provided. The required mathematical and statistical background for those relatively new to the Held is carefully outlined so that the material is accessible to a broad; range of levels.
1 Introduction 1
2 Background: Probability 13
3 Background: General inference 63
4 Background: Survival analysis 103
5 Marginal survival 129
6 Regression models and subject heterogeneity 151
7 Inference: Estimating equations 203
8 Inference: Functions of Brownian motion 231
9 Inference: Likelihood 267
10 Inference: Stochastic integrals 295
11 Inference: Small samples 311
12 Inference: Changepoint models 331
13 Explained variation 359
14 Explained randomness 407
15 Survival given covariates 437
16 Proofs of theorems, lemmas and corollaries 463
Bibliography