生存分析

內(nèi)容概要

　　《應(yīng)用統(tǒng)計學叢書·生存分析：模型與應(yīng)用
（英文版）》旨在系統(tǒng)地介紹生存分析的基本概念、理論設(shè)定和方法運用，重點在于通過SAS統(tǒng)計軟件對實際數(shù)據(jù)進行分析，深入淺出地描述生存分析的各類模型。書中涉及的統(tǒng)計方法包括Kaplan-Meirer估算法、各類參數(shù)回歸模型、Cox等比發(fā)生率模型、多向發(fā)生率模型和重復發(fā)生率模型、結(jié)構(gòu)性風險率模型以及一些生存分析方面的專題研究方法。
　　《應(yīng)用統(tǒng)計學叢書·生存分析：模型與應(yīng)用
（英文版）》著重于各類生存分析模型的實際運用，而不拘泥于模型的純理論推導，從而使對生存分析有興趣的科研人員以及大學生、研究生從中受益?！?/pre>


作者簡介
　　劉憲，1991年5月獲密歇根大學社會學博士學位，現(xiàn)任美國國防醫(yī)科大學（Uniformed Services
University of the Health
Sciences）精神病學系高級研究員、副教授及美國沃爾特里德國家軍事醫(yī)學中心（Walter Reed Army Medical
Center）研究員、高級統(tǒng)計師。在國際頂級刊物發(fā)表學術(shù)論文數(shù)十篇。截至2012年3月，所發(fā)表學術(shù)論文在國際各類刊物被引用1000多次。劉憲博士的主要研究領(lǐng)域為生存分析與死亡率交叉研究、縱向資料分析、創(chuàng)傷事件與精神疾病。


書籍目錄
Preface
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 Summar
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 AFI'
parameters
4.3.4 Illustration: A Weibull regression model on marital status
and survival among older Americans
4.4 Log-Iogistic regression models
4.4.1 Specifications of the log-logistic AFI' regression
model
4.4.2 Retransformation of AFT parameters to untransformed
log-logistic parameters
4.4.3 Illustration: The log-logistic regression model on mar:ital
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
……
6 Counting processes and diagnostics of the Cox model
7 Competing risks models and repeated events
8 Structural hazard rate regression models
9 Special topics
Appendix A The delta method


章節(jié)摘錄
版權(quán)頁：   插圖：   Another popular graphical method for checking the proportional hazards assumption in the Cox model is using the Arjas(1988)plots.Specifically,the Arjas plots are designed to make direct comparisons between observed and estimated event frequencies without adding a time-dependent variable.Therefore,this method is not based on the estimation of alternative models and only involves parameter estimates already derived from the partial likelihood procedure. According to Arjas(1988),the application of the stratified Cox model is subject to two types of defects:(1)an influential covariate may be deleted from the model(this defect has been discussed in Section 5.5 of this book)and(2)the stratified Cox model is based on the assumption of a common baseline hazard for all individuals,so that the individuals are stratified according to the baseline hazard.These two defects can seriously influence the efficiency of the Cox model,thus making it difficult to perform a graphical check correctly on the validity of the proportionality hypothesis.Accordingly,he proposes to test the pro-portionality assumption directly from the proportional hazard model including all(M+1)covariates. Practically,deriving the Arjas plots can be performed by taking the following steps.First,divide n individuals into K strata of the(M+1)th covariate according to the research interest of a particular study or previous findings.If the(M+1)th covariate is a continuous variable,classify the sample respondents into a few categories according to an existing theory or results from a previous empirical analysis.Second,calculate the estimated cumulative hazard rate at each observed survival time for each stratum using the parameter estimates obtained from the Cox model.Third,compute the cumulative number of actual events at each survival time for each stratum.Fourth,plot the estimated cumulative hazard rate at each actual survival time along the y axis against the corresponding observed cumulative number of events on the x axis for each stratum.Eventually,discrepancies between the estimated cumulative hazard rate and the empirical data can display whether the estimated hazard rates of those stata are scattered randomly or systematically too high or too low.




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《生存分析:模型與應(yīng)用(英文)》是由劉憲著，高等教育出版社出版的?！渡娣治?模型與應(yīng)用(英文)》著重于各類生存分析模型的實際運用，而不拘泥于模型的純理論推導，從而使對生存分析有興趣的科研人員以及大學生、研究生從中受益。





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