
Survival Analysis by David G Kleinbaum
This greatly expanded third edition of Survival Analysis- A Self-learning Text provides a highly readable description of state-of-the-art methods of analysis of survival/event-history data.-
Regression Methods in Biostatistics
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Bioinformatics and Computational Biology Solutions Using R and Bioconductor
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Mixed Effects Models and Extensions in Ecology with R
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Logistic Regression
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Applying Quantitative Bias Analysis to Epidemiologic Data
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Mathematical and Statistical Methods for Genetic Analysis
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Statistical Design and Analysis of Biological Experiments
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Capture-Recapture: Parameter Estimation for Open Animal Populations
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Applied Multivariate Statistics with R
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A Guide to QTL Mapping with R/qtl
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Statistical Inference on Residual Life
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Epidemiologic Studies in Cancer Prevention and Screening
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Likelihood and Bayesian Inference
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The Fundamentals of Modern Statistical Genetics
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The Analysis of Gene Expression Data
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Bayesian Methods in Structural Bioinformatics
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Analysis of Genetic Association Studies
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Statistical Methods for Dynamic Treatment Regimes
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Regression with Linear Predictors
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Statistical Analysis of Panel Count Data
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Forecasting the Health of Elderly Populations
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The Use of Restricted Significance Tests in Clinical Trials
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Cancer Mortality and Morbidity Patterns in the U.S. Population
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Design, Analysis, and Interpretation of Genome-Wide Association Scans
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Proportional Hazards Regression
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Epidemiology
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Adaptive Regression for Modeling Nonlinear Relationships
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Statistical Genetics of Quantitative Traits
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Statistical Learning in Genetics
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Analysing Seasonal Health Data
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Meta-Analysis
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Nonclinical Statistics for Pharmaceutical and Biotechnology Industries
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Modern Infectious Disease Epidemiology
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Statistical Methods for Disease Clustering
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Medical Applications of Finite Mixture Models
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Analyzing Medical Data Using S-PLUS
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Modeling Survival Data: Extending the Cox Model
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Likelihood, Bayesian, and MCMC Methods in Quantitative Genetics
From the book reviews:
“The authors present fundamental and basic ideas and methods of analysis of survival/event-history data from both applications and methodological points of view… This book is clearly written and well structured for a graduate course as well as for practitioners and consulting statisticians. … There are many good examples in this edition, and more importantly, this new edition offers additional exercises, making it a good candidate for adoption as a textbook.” (Technometrics, August, 2012)
"This text is … an elementary introduction to survival analysis. It is primarily intended for self-study, but it has also proven useful as a basic text in a standard classroom course … . Each chapter starts with an Introduction, an Abbreviated outline, and Objectives, and ends with self tests, exercises and a detailed outline. Solutions to tests and exercises are also provided." (Göran Broström, Zentralblatt MATH, Vol. 1093 (19), 2006)
"The most meaningful accolade that I can give to this text is that it admirably lives up to its title." Journal of the American Statistical Association, September 2006
"Imagine---a statistics textbook that actually explains things in English instead of explaining a topic by bombarding the reader with page-width equations requiring an advanced degree in Math just to read the book. If it weren't for this book, I would be really stuck." (David Britz)
| SKU | Unavailable |
| ISBN 13 | 9781441966452 |
| ISBN 10 | 1441966455 |
| Title | Survival Analysis |
| Author | David G Kleinbaum |
| Series | Statistics For Biology And Health |
| Condition | Unavailable |
| Binding Type | Hardback |
| Publisher | Springer-Verlag New York Inc. |
| Year published | 2011-08-31 |
| Number of pages | 700 |
| Cover note | Book picture is for illustrative purposes only, actual binding, cover or edition may vary. |
| Note | Unavailable |





































