Survival Analysis Using S: Analysis of Time-to-Event DataSurvival Analysis Using S: Analysis of Time-to-Event Data is designed as a text for a one-semester or one-quarter course in survival analysis for upper-level or graduate students in statistics, biostatistics, and epidemiology. Prerequisites are a standard pre-calculus first course in probability and statistics, and a course in applied linear regression models. No prior knowledge of S or R is assumed. A wide choice of exercises is included, some intended for more advanced students with a first course in mathematical statistics. The authors emphasize parametric log-linear models, while also detailing nonparametric procedures along with model building and data diagnostics. Medical and public health researchers will find the discussion of cut point analysis with bootstrap validation, competing risks and the cumulative incidence estimator, and the analysis of left-truncated and right-censored data invaluable. The bootstrap procedure checks robustness of cut point analysis and determines cut point(s). In a chapter written by Stephen Portnoy, censored regression quantiles - a new nonparametric regression methodology (2003) - is developed to identify important forms of population heterogeneity and to detect departures from traditional Cox models. By generalizing the Kaplan-Meier estimator to regression models for conditional quantiles, this methods provides a valuable complement to traditional Cox proportional hazards approaches. |
Contents
CHAPTER 1 Introduction | 1 |
CHAPTER 2 Nonparametric Methods | 25 |
CHAPTER 3 Parametric Methods | 55 |
CHAPTER 4 Regression Models | 95 |
CHAPTER 5 The Cox Proportional Hazards Model | 121 |
Data Diagnostics | 143 |
CHAPTER 7 Additional Topics | 181 |
CHAPTER 8 Censored Regression Quantiles | 213 |
| 247 | |
| 251 | |
Other editions - View all
Survival Analysis Using S: Analysis of Time-to-Event Data Mara Tableman,Jong Sung Kim Limited preview - 2003 |
Survival Analysis Using S: Analysis of Time-to-Event Data Mara Tableman,Jong Sung Kim No preview available - 2003 |
Common terms and phrases
analysis approach approximate assumption asymptotic called censored censored observations Chapter clinic coefficients compute conditional confidence consider continuous corresponding covariates Cox PH model curves death defined denote depends Derive deviance different discussed distribution effect equal error estimate event example exponential expression extended extreme failure Figure first fit given gives hazard function Hence included increases indicates individual intercept interest interval Know KPS.PRE likelihood linear log-logistic log(T maintained mean measure median method normal Note object observations obtain output p-value parameter patients plot presented probability proportional provides Q-Q plot random ratio regression model regression quantile residuals respectively risk sample scale shows significant standard statistic survival survivor function Table treatment truncation uncensored variable variance weeks Weibull weight zero
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