Applied Survival Analysis Using R
eBook - PDF

Applied Survival Analysis Using R

  1. English
  2. PDF
  3. Available on iOS & Android
eBook - PDF

Applied Survival Analysis Using R

About this book

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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Yes, you can access Applied Survival Analysis Using R by Dirk F. Moore in PDF and/or ePUB format, as well as other popular books in Medicine & Probability & Statistics. We have over one million books available in our catalogue for you to explore.

Information

Table of contents

  1. Preface
  2. Contents
  3. 1 Introduction
  4. 2 Basic Principles of Survival Analysis
  5. 3 Nonparametric Survival Curve Estimation
  6. 4 Nonparametric Comparison of Survival Distributions
  7. 5 Regression Analysis Using the Proportional Hazards Model
  8. 6 Model Selection and Interpretation
  9. 7 Model Diagnostics
  10. 8 Time Dependent Covariates
  11. 9 Multiple Survival Outcomes and Competing Risks
  12. 10 Parametric Models
  13. 11 Sample Size Determination for Survival Studies
  14. 12 Additional Topics
  15. Erratum to
  16. A A Basic Guide to Using R for Survival Analysis
  17. Index
  18. R Package Index