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

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.

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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.