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Modelling Survival Data in Medical Research, Second Edition 2nd New edition [Minkštas viršelis]

4.56/5 (31 ratings by Goodreads)
(NHS Blood and Transplant, UK)
  • Formatas: Paperback / softback, 410 pages, aukštis x plotis: 234x156 mm, weight: 590 g, 83 Illustrations, black and white, Contains 57 paperbacks
  • Serija: Chapman & Hall/CRC Texts in Statistical Science
  • Išleidimo metai: 28-Mar-2003
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1584883251
  • ISBN-13: 9781584883258
Kitos knygos pagal šią temą:
Modelling Survival Data in Medical Research, Second Edition 2nd New editionDidesnis paveikslėlis
  • Formatas: Paperback / softback, 410 pages, aukštis x plotis: 234x156 mm, weight: 590 g, 83 Illustrations, black and white, Contains 57 paperbacks
  • Serija: Chapman & Hall/CRC Texts in Statistical Science
  • Išleidimo metai: 28-Mar-2003
  • Leidėjas: Chapman & Hall/CRC
  • ISBN-10: 1584883251
  • ISBN-13: 9781584883258
Kitos knygos pagal šią temą:
Pastovi nuoroda: https://www.kriso.lt/db/9781584883258.html
Raktažodžiai:
A graduate or undergraduate textbook designed primarily for students studying statistics in a pharmaceutical or medical research context, but should also be accessible to numerate scientists and clinicians working with statisticians. The 1993 first edition has been updated to incorporate current statistical practice and computer software. Annotation (c) Book News, Inc., Portland, OR (booknews.com)

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.All of the data sets used in the book are available for download from www.crcpress.com/e_products/downloads. 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 practices.

Recenzijos

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 statisticians 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 Statistician

Preface to the second edition
Preface to the first edition
Survival analysis
1(14)
Special features of survival data
1(4)
Some examples
5(6)
Survivor function and hazard function
11(2)
Further reading
13(2)
Some non-parametric procedures
15(40)
Estimating the survivor function
15(8)
Standard error of the estimated survivor function
23(6)
Estimating the hazard function
29(4)
Estimating the median and percentiles of survival times
33(2)
Confidence intervals for the median and percentiles
35(2)
Comparison of two groups of survival data
37(11)
Comparison of three or more groups of survival data
48(1)
Stratified tests
49(2)
Log-rank test for trend
51(2)
Further reading
53(2)
Modelling survival data
55(56)
Modelling the hazard function
55(3)
The linear component of the proportional hazards model
58(5)
Fitting the proportional hazards model
63(6)
Confidence intervals and hypothesis tests for the β's
69(4)
Comparing alternative models
73(7)
Strategy for model selection
80(9)
Interpretation of parameter estimates
89(8)
Estimating the hazard and survivor functions
97(9)
Proportional hazards modelling and the log-rank test
106(3)
Further reading
109(2)
Model checking in the Cox regression model
111(40)
Residuals for the Cox regression model
111(10)
Assessment of model fit
121(10)
Identification of influential observations
131(10)
Testing the assumption of proportional hazards
141(7)
Recommendations
148(1)
Further reading
149(2)
Parametric proportional hazards models
151(44)
Models for the hazard function
151(4)
Assessing the suitability of a parametric model
155(3)
Fitting a parametric model to a single sample
158(10)
A model for the comparison of two groups
168(7)
The Weibull proportional hazards model
175(8)
Comparing alternative Weibull models
183(7)
The Gompertz proportional hazards model
190(2)
Model choice
192(1)
Further reading
193(2)
Accelerated failure time and other parametric models
195(36)
Probability distributions for survival data
195(4)
Exploratory analyses
199(1)
The accelerated failure time model for comparing two groups
200(6)
The general accelerated failure time model
206(3)
Parametric accelerated failure time models
209(7)
Fitting and comparing accelerated failure time models
216(7)
The proportional odds model
223(4)
Some other distributions for survival data
227(1)
Further reading
228(3)
Model checking in parametric models
231(20)
Residuals for parametric models
231(3)
Residuals for particular parametric models
234(6)
Comparing observed and fitted survivor functions
240(2)
Identification of influential observations
242(5)
Testing proportional hazards in the Weibull model
247(1)
Further reading
248(3)
Time-dependent variables
251(22)
Types of time-dependent variables
251(1)
A model with time-dependent variables
252(6)
Model comparison and validation
258(2)
Some applications of time-dependent variables
260(2)
Three examples
262(9)
Further reading
271(2)
Interval-censored survival data
273(26)
Modelling interval-censored survival data
273(3)
Modelling the recurrence probability in the follow-up period
276(3)
Modelling the recurrence probability at different times
279(7)
Arbitrarily interval-censored survival data
286(10)
Parametric models for interval-censored data
296(1)
Discussion
297(1)
Further reading
297(2)
Sample size requirements for a survival study
299(14)
Distinguishing between two treatment groups
299(1)
Calculating the required number of deaths
300(6)
Calculating the required number of patients
306(5)
Further reading
311(2)
Some additional topics
313(18)
Non-proportional hazards
313(5)
Informative censoring
318(2)
Frailty models
320(3)
Multistate models
323(4)
Effect of covariate adjustment
327(1)
Measures of explained variation
328(1)
Modelling a cure probability
329(1)
Some other designs in survival analysis
329(2)
Computer software for survival analysis
331(22)
Use of SAS in survival analysis
331(4)
Illustration of the use of SAS
335(11)
Use of SAS in some other analyses
346(6)
Further reading
352(1)
Appendix A Maximum likelihood estimation
353(4)
A.1 Inference about a single unknown parameter
353(2)
A.2 Inference about a vector of unknown parameters
355(2)
Appendix B Likelihood function for randomly censored data
357(2)
Appendix C Standard error of percentiles
359(4)
C.1 Standard error of a percentile of the Weibull distribution
359(1)
C.2 Standard error of a percentile in the Weibull model
360(2)
C.3 Standard error of a percentile in the AFT model
362(1)
Appendix D Additional data sets
363(8)
D.1 Chronic active hepatitis
363(1)
D.2 Recurrence of bladder cancer
364(1)
D.3 Survival of black ducks
364(3)
D.4 Bone marrow transplantation
367(1)
D.5 Chronic granulomatous disease
367(4)
References 371(12)
Index of examples 383(2)
Index 385