Modelling Survival Data in Medical Research

Modelling Survival Data in Medical Research

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Critically acclaimed and resoundingly popular in its first edition, Modelling Survival Data in Medical Research has been thoroughly revised and updated to reflect the many developments and advances--particularly in software--made in the field over the last 10 years. Now, more than ever, it provides an outstanding text for upper-level and graduate courses in survival analysis, biostatistics, and time-to-event analysis.The treatment begins with an introduction to survival analysis and a description of four studies that lead to survival data. Subsequent chapters then use those data sets and others to illustrate the various analytical techniques applicable to such data, including the Cox regression model, the Weibull proportional hazards model, and others. This edition features a more detailed treatment of topics such as parametric models, accelerated failure time models, and analysis of interval-censored data. The author also focuses the software section on the use of SAS, summarising the methods used by the software to generate its output and examining that output in detail. Profusely illustrated with examples and written in the author's trademark, easy-to-follow style, Modelling Survival Data in Medical Research, Second Edition is a thorough, practical guide to survival analysis that reflects current statistical more

Product details

  • Paperback | 410 pages
  • 156 x 234 x 22mm | 580.6g
  • Taylor & Francis Inc
  • Chapman & Hall/CRC
  • Boca Raton, FL, United States
  • English
  • Revised
  • 2nd Revised edition
  • 83 black & white illustrations
  • 1584883251
  • 9781584883258
  • 859,490

Review quote

Collett has succeeded admirably in updating the first edition of his book ... [This book] has numerous, carefully worked, real-data examples. There is enough new material in the second edition to justify its purchase by someone who already owns the first edition. -Journal of the American Statistical Association, Sept. 2004, Vol. 99, No. 467 this text is a fine example of technical writing and remains highly recommended for both students and researchers requiring an introduction to survival analysis in a medical context. -Journal of the Royal Statistical Society, Issue 167 (4) ... a well written practical guide with a demonstration of SAS software to perform survival analysis. ... It can be used as a textbook in a graduate-level survival analysis course ... . -Journal of Statistical Computation & Simulation, Vol. 74, No. 5, May 2004 It is thorough and authoritative, covers all essential theory and contains many practical tips. -Journal of the Royal Statistical Society, Vol. 157 Praise for the First Edition: ... a useful book that has particular merit for the applied statistician. Chapters 1-6 and 11 alone supply a wonderful introduction to survival analysis. The mathematical statistician unfamiliar with survival analysis who desires to become quickly abreast will also gain much from the book. -Journal of the American Statistical Association Students found the presentation of the material and examples to be very helpful ... an excellent book ... I highly recommend this book for practising statisticians engaged in analysing univariate survival data. ... This book will not only serve the statistical practitioner in the medical and pharmaceutical research areas well, but will be a convenient text for the lecturer aiming to include a useful applied component into a post-graduate statistics or operational research degree course. -Journal of the Royal Statistical Society The book would be a popular text for courses and a well-thumbed addition to any medical statistician's collection. It is sufficiently general to be of interest to industrial statisticians concerned with lifetime testing but the focus is clearly on survival of patients under treatment. -The Statisticianshow more

Table of contents

Survival Analysis. Some Non-Parametric Procedures. Modelling Survival Data. Model Checking in the Cox Regression Model. Parametric Proportional Hazards Models. Accelerated Failure Time and other Parametric Models. Model Checking in Parametric Models. Time-Dependent Variables. Interval-Censored Survival Data. Sample Size Requirements for a Survival Study. Some Additional Topics. Computer Software for Survival Analysis. Appendices. References. Index of Examples. more

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