Analysing Survival Data from Clinical Trials and Observational Studies

A practical guide to methods of survival analysis for medical researchers with limited statistical experience. Methods and techniques described range from descriptive and exploratory analysis to multivariate regression methods. Uses illustrative data from actual clinical trials and observational studies to describe methods of analysing and reporting results. Also reviews the features and performance of statistical software available for applying the methods of analysis discussed. Preface.

Series Preface.

1. The Scope of Survival Analysis.

2. Randomized Clinical Trials: General Principles and Some Controversial Issues.

3. Estimation of Survival Probabilities.

4. Non-Parametric Methods for the Comparison of Survival Curves.

5. Distribution Functions for Failure Time T .

6. The Cox Regression Model.

7. Validation of the Proportional Hazards Models.

8. Parametric Regression Models.

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

10. Competing Risks.

11. Meta-Analysis.

References.

Author Index.

Subject Index.

“…a valuable resource for both practitioners and students alike.” (Journal of the Royal Statistical Society, Series A, Vol.168, No.2, March 2005)

Ettore Marubini is the author of Analysing Survival Data from Clinical Trials and Observational Studies, published by Wiley.

Maria Grazia Valsecchi is the author of Analysing Survival Data from Clinical Trials and Observational Studies, published by Wiley.

Originally published in 1995, Analysing Survival Data from Clinical Trials and Observational Studies provides a thorough yet accessible overview of survival data analysis. It is written in a highly accessible style focussed on concepts and methods rather than theory, and includes careful explanation of the underlying statistical and medical principles. Now available in paperback, the book provides a comprehensive introduction to the subject suitable for graduate students of biostatistics and survival analysis.

• Provides a practical introduction to survival data analysis.
• Covers the core topics, including estimation of survival probabilities, as well as more advance topics, such as parametric regression models, competing risk and meta-analysis.
• Illustrates the methods using real data sets throughout.
• Written in a lucid style, suitable for students of biostatistics and survival analysis.
• Includes discussion of a range of software choices for applying the methods described.

Analysing Survival Data from Clinical Trials and Observational Studies is ideally suited to graduate students studying courses in survival analysis. The wide range of examples and applications make it an ideal practical reference for researchers and practitioners working in survival analysis from statistics, medicine and epidemiology.