Survival Analysis

Proportional and Non-Proportional Hazards Regression
1. Auflage 2021
ISBN: 978-3-030-33441-3
Verlag: Springer International Publishing

Buch, Englisch, 475 Seiten, Format (B × H): 155 mm x 235 mm, Gewicht: 828 g

This book provides an extensive coverage of the methodology of survival analysis, ranging from introductory level material to deeper more advanced topics. The framework is that of proportional and non-proportional hazards models; a structure that is broad enough to enable the recovery of a large number of established results as well as to open the way to many new developments. The emphasis is on concepts and guiding principles, logical and graphical. Formal proofs of theorems, propositions and lemmas are gathered together at the end of each chapter separate from the main presentation. 

The intended audience includes academic statisticians, biostatisticians, epidemiologists and also researchers in these fields whose focus may be more on the applications than on the theory. The text could provide the basis for a two semester course on survival analysis and, with this goal in mind, each chapter includes a section with a range of exercises as a teaching aid for instructors. 

Zielgruppe

Graduate

Autoren/Hrsg.

O'Quigley, John

Fachgebiete

• Mathematik | Informatik EDV | Informatik Programmierung | Softwareentwicklung Algorithmen & Datenstrukturen
• Mathematik | Informatik Mathematik Stochastik Stochastische Prozesse
• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
• Mathematik | Informatik Mathematik Stochastik Wahrscheinlichkeitsrechnung
• Mathematik | Informatik Mathematik Stochastik Bayesianische Inferenz
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle

Inhaltsverzeichnis

Introduction.- Survival analysis.- Survival without covariates.- Proportional hazards models.- Proportional hazards models in epidemiology.- Non-proportional hazards models.- Estimating equations.- Survival given covariate information.- Regression effect process.- Model construction guided by regression effect process.- Hypothesis tests.

Über Autor(innen)

John O’Quigley is professor of statistics in the Department of Statistical Science, University College London. Professor O'Quigley's academic career began as MRC research scientist at the University of Leeds, U.K., a stint followed by several positions in France, the most recent being in the Laboratory of Probability, Statistics and Modelling, University of Paris – Sorbonne. The most significant part of Professor O’Quigley’s career has been spent in the United States, mainly in the Department of Mathematics, University of California San Diego during which time he was a tenured full professor of mathematics. He was also Director of the Laboratory for Mathematics and Statistics. Prior to that Professor O’Quigley was a researcher at the Fred Hutchinson Cancer Research Center and the Department of Biostatistics, University of Washington, Seattle. Several years were also spent as full professor at the University of Virginia Medical School as well as the Department of Mathematics at Lancaster University, U.K.