## Applied Survival Analysis: Time-to-EventA Practical, Up-To-Date Guide To Modern Methods In The Analysis Of Time To Event Data. The rapid proliferation of powerful and affordable statistical software packages over the past decade has inspired the development of an array of valuable new methods for analyzing survival time data. Yet there continues to be a paucity of statistical modeling guides geared to the concerns of health-related researchers who study time to event data. This book helps bridge this important gap in the literature. Applied Survival Analysis is a comprehensive introduction to regression modeling for time to event data used in epidemiological, biostatistical, and other health-related research. Unlike other texts on the subject, it focuses almost exclusively on practical applications rather than mathematical theory and offers clear, accessible presentations of modern modeling techniques supplemented with real-world examples and case studies. While the authors emphasize the proportional hazards model, descriptive methods and parametric models are also considered in some detail. Key topics covered in depth include: * Variable selection. * Identification of the scale of continuous covariates. * The role of interactions in the model. * Interpretation of a fitted model. * Assessment of fit and model assumptions. * Regression diagnostics. * Recurrent event models, frailty models, and additive models. * Commercially available statistical software and getting the most out of it. Applied Survival Analysis is an ideal introduction for graduate students in biostatistics and epidemiology, as well as researchers in health-related fields. |

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### Contents

Descriptive Methods for Survival Data | 27 |

Regression Models for Survival Data | 87 |

Interpretation of a Fitted Proportional Hazards Regression Model | 113 |

Copyright | |

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### Common terms and phrases

95 percent confidence Aalen accelerated failure analysis assess baseline hazard function Beck score BECKTOTA Borgan Chapter computed confidence interval confidence interval estimates continuous covariates counting process cumulative regression coefficient data set delta method denoted discussed distribution endpoints equation esti Estimated Coefficients estimated hazard ratio estimated survivorship function example exponential regression model Figure fitted model follow-up graph hazard function HMO-HIV+ study interaction interpretation interval estimates ith subject IVHX_3 Kaplan—Meier estimator likelihood function likelihood ratio test linear regression log-logistic logistic regression martingale residuals methods model fit model in Table months NDRUGTX nested case-control study observed survival obtained parameter partial likelihood ratio percent confidence interval plot present previous drug treatments proportional hazards model return to drug risk score risk set score test shown in Table significant software packages Standard Errors survival analysis time-varying covariates tion Two-Tailed p-Values Variable Coeff variance Wald test Weibull regression model yields z-Scores