Free Ebook Analysing Survival Data from Clinical Trials and Observational Studies By Ettore Marubini, Maria Grazia Valsecchi
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Analysing Survival Data from Clinical Trials and Observational Studies By Ettore Marubini, Maria Grazia Valsecchi
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- Sales Rank: #319802 in Books
- Published on: 1995-06-06
- Original language: English
- Number of items: 1
- Dimensions: 9.55" h x 1.17" w x 6.36" l, 1.85 pounds
- Binding: Hardcover
- 430 pages
Review
"...this book is written well..." (Statistics in Medical Research, Vol.12, No. 2, 2003)
From the Publisher
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.
From the Back Cover
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.
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