Analysing Survival Data from Clinical Trials and Observational Studies

ISBN-13: 9780471939870 / Angielski / Twarda / 1995 / 432 str.

M. Emmerson; Maria G. Valsecchi; Ettore Marubini

Opis

The statistical methods and techniques described in this text range from descriptive and exploratory analysis to multivariate regression methods. Also discussed are the features and performance levels of statistical software packages that exist to apply the methods of analysis. Illustrative data sets are used throughout to describe methods of analysis and to report on results.

Recenzje

"...this book is written well..." (Statistics in Medical Research, Vol.12, No. 2, 2003)

Spis treści

The Scope of Survival Analysis.

Randomized Clinical Trials: General Principles and Some Controversial Issues.

Estimation of Survival Probabilities.

Non–Parametric Methods for the Comparison of Survival Curves.

Distribution Functions for Failure Time T .

The Cox Regression Model.

Validation of the Proportional Hazards Models.

Parametric Regression Models.

The Study of Prognostic Factors and the Assessment of Treatment Effect.

Competing Risks.

Meta–Analysis.

References.

Indexes.

Blurb

Statistics in Practice A new series of practical books outlining the use of statistical techniques in a wide range of application areas:
∗ Human and Biological Sciences
∗ Earth and Environmental Sciences
∗ Industry, Commerce and Finance
Analysing Survival Data from Clinical Trials and Observational Studies Ettore Marubini and Maria Grazia Valsecchi Institute of Medical Statistics and Biometry, University of Milan, Italy Analysing Survival Data from Clinical Trials and Observational Studies provides a thorough yet accessible, practical guide for medical research professionals with limited statistical experience, with careful explanations of the underlying statistical and scientific principles which will be useful to clinicians and biostatisticians. Emphasising the concepts and methods rather than the theoretical background, the book covers
∗ Estimation of survival curves.
∗ Non–parametric methods for comparison of survival curves.
∗ The Cox regression model.
∗ The parametric regression model.
∗ The study of prognostic factors.
∗ Competing risks.
Alternative data sets, taken from important real–life medical examples, are used throughout to demonstrate methods of analysis and reporting of results.