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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.
Introduction.- Background: probability.- Background: inference.- Background: survival analysis.- Marginal survival.- Regression models and subject heterogeneity.- Estimating equations.- Inference: functions of the Brownian motion.- Inference: likelihood.- Inference: counting processes.- Inference: small samples.- Inference: changepoint models.- Explained variation.- Explained randomness.- Survival given covariates.- Proofs of theorems, lemmas and corollaries.
From the reviews: "The book is clearly intended to be student-friendly. Each chapter begins with a section called Summary and a following one called motivation; each chapter ends with some exercises and class projects. ! It is very carefully written, with detailed explanation and discussion everywhere. ! I believe that the book can be thoroughly recommended to the student starting his research in the field and to the practitioner who needs to understand some of the theory." (Martin Crowder, International Statistical Review, Vol. 76 (3), 2008)
