Applied Survival Analysis Using R

Front Cover
Springer, May 11, 2016 - Medical - 226 pages
Applied Survival Analysis Using R covers the main principles of survival analysis, gives examples of how it is applied, and teaches how to put those principles to use to analyze data using R as a vehicle. Survival data, where the primary outcome is time to a specific event, arise in many areas of biomedical research, including clinical trials, epidemiological studies, and studies of animals. Many survival methods are extensions of techniques used in linear regression and categorical data, while other aspects of this field are unique to survival data. This text employs numerous actual examples to illustrate survival curve estimation, comparison of survivals of different groups, proper accounting for censoring and truncation, model variable selection, and residual analysis.
Because explaining survival analysis requires more advanced mathematics than many other statistical topics, this book is organized with basic concepts and most frequently used procedures covered in earlier chapters, with more advanced topics near the end and in the appendices. A background in basic linear regression and categorical data analysis, as well as a basic knowledge of calculus and the R system, will help the reader to fully appreciate the information presented. Examples are simple and straightforward while still illustrating key points, shedding light on the application of survival analysis in a way that is useful for graduate students, researchers, and practitioners in biostatistics.
 

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Contents

1 Introduction
1
2 Basic Principles of Survival Analysis
11
3 Nonparametric Survival Curve Estimation
25
4 Nonparametric Comparison of Survival Distributions
43
5 Regression Analysis Using the Proportional Hazards Model
55
6 Model Selection and Interpretation
73
7 Model Diagnostics
87
8 Time Dependent Covariates
101
10 Parametric Models
137
11 Sample Size Determination for Survival Studies
156
12 Additional Topics
177
Erratum to
E-1
A A Basic Guide to Using R for Survival Analysis
201
Index
222
R Package Index
225
Copyright

9 Multiple Survival Outcomes and Competing Risks
112

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About the author (2016)

Dirk F. Moore is Associate Professor of Biostatistics at the Rutgers School of Public Health and the Rutgers Cancer Institute of New Jersey. He received a Ph.D. in biostatistics from the University of Washington in Seattle and, prior to joining Rutgers, was a faculty member in the Statistics Department at Temple University. He has published numerous papers on the theory and application of survival analysis and other biostatistics methods to clinical trials and epidemiology studies.

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