# Applied Survival Analysis Using R

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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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.