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生存分析：模型与应用 (英文版)

样章
作者：
刘宪
定价：
79.00元
ISBN：
978-7-04-034826-2
版面字数：
740.000千字
开本：
16开
全书页数：
446页
装帧形式：
平装
重点项目：
暂无
出版时间：
2012-07-27
读者对象：
学术著作
一级分类：
自然科学
二级分类：
统计学
三级分类：
统计理论和方法

《应用统计学丛书·生存分析：模型与应用 （英文版）》旨在系统地介绍生存分析的基本概念、理论设定和方法运用，重点在于通过SAS统计软件对实际数据进行分析，深入浅出地描述生存分析的各类模 型。书中涉及的统计方法包括Kaplan-Meirer估算法、各类参数回归模型、Cox等比发生率模型、多向发生率模型和重复发生率模型、结构性风险率 模型以及一些生存分析方面的专题研究方法。

《应用统计学丛书·生存分析：模型与应用 （英文版）》着重于各类生存分析模型的实际运用，而不拘泥于模型的纯理论推导，从而使对生存分析有兴趣的科研人员以及大学生、研究生从中受益。

  • 前辅文
  • 1 Introduction
    • 1.1 What is survival analysis and how is it applied?
    • 1.2 The history of survival analysis and its progress
    • 1.3 General features of survival data structure
    • 1.4 Censoring
      • 1.4.1 Mechanisms of right censoring
      • 1.4.2 Left censoring, interval censoring, and left truncation
    • 1.5 Time scale and the origin of time
      • 1.5.1 Observational studies
      • 1.5.2 Biomedical studies
      • 1.5.3 Health care utilization
    • 1.6 Basic lifetime functions
      • 1.6.1 Continuous lifetime functions
      • 1.6.2 Discrete lifetime functions
      • 1.6.3 Basic likelihood functions for right, left, and interval censoring
    • 1.7 Organization of the book and data used for illustrations
    • 1.8 Criteria for performing survival analysis
  • 2 Descriptive approaches of survival analysis
    • 2.1 The Kaplan-Meier (product-limit) and Nelson-Aalen estimators
      • 2.1.1 Kaplan-Meier estimating procedures with or without censoring
      • 2.1.2 Formulation of the Kaplan-Meier and Nelson-Aalen estimators
      • 2.1.3 Variance and standard error of the survival function
      • 2.1.4 Confidence intervals and confidence bands of the survival function
    • 2.2 Life table methods
      • 2.2.1 Life table indicators
      • 2.2.2 Multistate life tables
      • 2.2.3 Illustration: Life table estimates for older Americans
    • 2.3 Group comparison of survival functions
      • 2.3.1 Logrank test for survival curves of two groups
      • 2.3.2 The Wilcoxon rank sum test on survival curves of two groups
      • 2.3.3 Comparison of survival functions for more than two groups
      • 2.3.4 Illustration: Comparison of survival curves between married　and unmarried persons
    • 2.4 Summary
  • 3 Some popular survival distribution functions
    • 3.1 Exponential survival distribution
    • 3.2 The Weibull distribution and extreme value theory
      • 3.2.1 Basic specifications of the Weibull distribution
      • 3.2.2 The extreme value distribution
    • 3.3 Gamma distribution
    • 3.4 Lognormal distribution
    • 3.5 Log-logistic distribution
    • 3.6 Gompertz distribution and Gompertz-type hazard models
    • 3.7 Hypergeometric distribution
    • 3.8 Other distributions
    • 3.9 Summary
  • 4 Parametric regression models of survival analysis
    • 4.1 General specifications and inferences of parametric regression models
      • 4.1.1 Specifications of parametric regression models on the hazard function
      • 4.1.2 Specifications of accelerated failure time regression models
      • 4.1.3 Inferences of parametric regression models and likelihood functions
      • 4.1.4 Procedures of maximization and hypothesis testing on ML estimates
    • 4.2 Exponential regression models
      • 4.2.1 Exponential regression model on the hazard function
      • 4.2.2 Exponential accelerated failure time regression model
      • 4.2.3 Illustration: Exponential regression model on marital status and survival among older Americans
    • 4.3 Weibull regression models
      • 4.3.1 Weibull hazard regression model
      • 4.3.2 Weibull accelerated failure time regression model
      • 4.3.3 Conversion of Weibull proportional hazard and AFT parameters
      • 4.3.4 Illustration: A Weibull regression model on marital status and survival among older Americans
    • 4.4 Log-logistic regression models
      • 4.4.1 Specifications of the log-logistic AFT regression model
      • 4.4.2 Retransformation of AFT parameters to untransformed log-logistic parameters
      • 4.4.3 Illustration: The log-logistic regression model on marital status and survival among the oldest old Americans
    • 4.5 Other parametric regression models
      • 4.5.1 The lognormal regression model
      • 4.5.2 Gamma distributed regression models
    • 4.6 Parametric regression models with interval censoring
      • 4.6.1 Inference of parametric regression models with interval censoring
      • 4.6.2 Illustration: A parametric survival model with independent interval censoring
    • 4.7 Summary
  • 5 The Cox proportional hazard regression model and advances
    • 5.1 The Cox semi-parametric hazard model
      • 5.1.1 Basic specifications of the Cox proportional hazard model
      • 5.1.2 Partial likelihood
      • 5.1.3 Procedures of maximization and hypothesis testing on partial likelihood
    • 5.2 Estimation of the Cox hazard model with tied survival times
      • 5.2.1 The discrete-time logistic regression model
      • 5.2.2 Approximate methods handling ties in the proportional hazard model
      • 5.2.3 Illustration on tied survival data: Smoking cigarettes and the mortality of older Americans
    • 5.3 Estimation of survival functions from the Cox proportional hazard model
      • 5.3.1 The Kalbfleisch-Prentice method
      • 5.3.2 The Breslow method
      • 5.3.3 Illustration: Comparing survival curves for smokers and nonsmokers among older Americans
    • 5.4 The hazard rate model with time-dependent covariates
      • 5.4.1 Categorization of time-dependent covariates
      • 5.4.2 The hazard rate model with time-dependent covariates
      • 5.4.3 Illustration: A hazard model on time-dependent marital status and the mortality of older Americans
    • 5.5 Stratified proportional hazard rate model
      • 5.5.1 Specifications of the stratified hazard rate model
      • 5.5.2 Illustration: Smoking cigarettes and the mortality of older Americans with stratification on three age groups
    • 5.6 Left truncation, left censoring, and interval censoring
      • 5.6.1 The Cox model with left truncation, left censoring, and interval censoring
      • 5.6.2 Illustration: Analyzing left truncated survival data on smoking cigarettes and the mortality of unmarried older Americans
    • 5.7 Qualitative factors and local tests
      • 5.7.1 Qualitative factors and scaling approaches
      • 5.7.2 Local tests
      • 5.7.3 Illustration of local tests: Educational attainment and the mortality of older Americans
    • 5.8 Summary
  • 6 Counting processes and diagnostics of the Cox model
    • 6.1 Counting processes and the martingale theory
      • 6.1.1 Counting processes
      • 6.1.2 The martingale theory
      • 6.1.3 Stochastic integrated processes as martingale transforms
      • 6.1.4 Martingale central limit theorems
      • 6.1.5 Counting process formulation for the Cox model
    • 6.2 Residuals of the Cox proportional hazard model
      • 6.2.1 Cox-Snell residuals
      • 6.2.2 Schoenfeld residuals
      • 6.2.3 Martingale residuals
      • 6.2.4 Score residuals
      • 6.2.5 Deviance residuals
      • 6.2.6 Illustration: Residual analysis on the Cox model of smoking cigarettes and the mortality of older Americans
    • 6.3 Assessment of proportional hazards assumption
      • 6.3.1 Checking proportionality by adding a time-dependent variable
      • 6.3.2 The Andersen plots for checking proportionality
      • 6.3.3 Checking proportionality with scaled Schoenfeld residuals
      • 6.3.4 The Arjas plots
      • 6.3.5 Checking proportionality with cumulative sums of martingale-based residuals
      • 6.3.6 Illustration: Checking the proportionality assumption in the Cox model for the effect of age on the mortality of older Americans
    • 6.4 Checking the functional form of a covariate
      • 6.4.1 Checking model fit statistics for different link functions
      • 6.4.2 Checking the functional form with cumulative sums of martingale-based residuals
      • 6.4.3 Illustration: Checking the functional form of age in the Cox model on the mortality of older Americans
      • 6.5 Identification of influential observations in the Cox model
      • 6.5.1 The likelihood displacement statistic approximation
      • 6.5.2 LMAX statistic for identification of influential observations
      • 6.5.3 Illustration: Checking influential observations in the Cox model on the mortality of older Americans
    • 6.6 Summary
  • 7 Competing risks models and repeated events
    • 7.1 Competing risks hazard rate models
      • 7.1.1 Latent failure times of competing risks and model specifications
      • 7.1.2 Competing risks models and the likelihood function without covariates
      • 7.1.3 Inference for competing risks models with covariates
      • 7.1.4 Competing risks model using the multinomial logit regression
      • 7.1.5 Competing risks model with dependent failure types
      • 7.1.6 Illustration of competing risks models: Smoking cigarettes and the mortality of older Americans from three causes of death
    • 7.2 Repeated events
      • 7.2.1 Andersen and Gill model (AG)
      • 7.2.2 PWP total time and gap time models (PWP-CP and PWP-GT)
      • 7.2.3 The WLW model and extensions
      • 7.2.4 Proportional rate and mean functions of repeated events
      • 7.2.5 Illustration: The effects of a medical treatment on repeated patient visits
    • 7.3 Summary
  • 8 Structural hazard rate regression models
    • 8.1 Some thoughts about the structural hazard regression models
    • 8.2 Structural hazard rate model with retransformation of random errors
      • 8.2.1 Model specification
      • 8.2.2 The estimation of the full model
      • 8.2.3 The estimation of reduced-form equations
      • 8.2.4 Decomposition of causal effects on hazard rates and survival functions
      • 8.2.5 Illustration: The effects of veteran status on the mortality of older Americans and its pathways
    • 8.3 Summary
  • 9 Special topics
    • 9.1 Informative censoring
      • 9.1.1 Selection model
      • 9.1.2 Sensitivity analysis models
      • 9.1.3 Comments on current models handling informative censoring
    • 9.2 Bivariate and multivariate survival functions
      • 9.2.1 Inference of the bivariate survival model
      • 9.2.2 Estimation of bivariate and multivariate survival models
      • 9.2.3 Illustration of marginal models handling multivariate survival data
    • 9.3 Frailty models
      • 9.3.1 Hazard models with individual frailty
      • 9.3.2 The correlated frailty model
      • 9.3.3 Illustration of frailty models: The effect of veteran status on the mortality of older Americans revisited
    • 9.4 Mortality crossovers and the maximum life span
      • 9.4.1 Basic specifications
      • 9.4.2 Relative acceleration of the hazard rate and timing of mortality crossing
      • 9.4.3 Mathematical conditions for maximum life span and mortality crossover
    • 9.5 Survival convergence and the preceding mortality crossover
      • 9.5.1 Mathematical proofs for survival convergence and mortality crossovers
      • 9.5.2 Simulations
      • 9.5.3 Explanations for survival convergence and the preceding mortality crossover
      • 9.6 Sample size required and power analysis
      • 9.6.1 Calculation of sample size required
      • 9.6.2 Illustration: Calculating sample size required
    • 9.7 Summary
  • Appendix A The delta method
  • Appendix B Approximation of the variance-covariance matrix for the predicted probabilities from results of the multinomial logit model
  • Appendix C Simulated patient data on treatment of PTSD (n=255)
  • Appendix D SAS code for derivation of φ estimates in reduced-form equations
  • Appendix E The analytic result of k*(x)
  • References
  • Index

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