## Applied Survival Analysis Using RApplied 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 | |

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 |

222 | |

225 | |

9 Multiple Survival Outcomes and Competing Risks | 112 |

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### Common terms and phrases

accrual ageGroup4 Applied Survival Analysis baseline hazard causes censoring indicator chapter clinical trial coef exp(coef competing risks compute confidence interval covariates Cox model Cox proportional hazards coxph cumulative incidence functions data frame data set death from prostate define delta discussed employment example exponential distribution factor failure follows found at DOI hazard function hazard ratio Kaplan-Meier estimate left truncation likelihood function likelihood ratio test linear log partial likelihood log-likelihood log-rank test Martingale residuals maximum likelihood estimate method mutant number of deaths number of events number of patients obtain p-value package parameter estimates piecewise exponential plot predictors probability of death proportional hazards model prost prostate cancer random regression relapse result risk set se(coef Sect status Surv survival analysis survival curve estimate survival data survival distribution survival function survTime test statistic transplant treatment group updated variable vector Wald test Weibull distribution x.vec