A01=Jong Sung Kim
A01=Mara Tableman
Accelerated Failure Time Model
advanced survival modeling for graduate students
Al Ti
Author_Jong Sung Kim
Author_Mara Tableman
biostatistics methods
bootstrap validation techniques
Category=PBT
censored data analysis
Coef Exp
competing risks estimation
Conditional Quantile
Cox PH
Cox PH Model
Cox Snell Residuals
Da Ta
Data
Data Set
Deviance Residuals
Empirical Quantile Function
epidemiological modeling
eq_isMigrated=1
eq_isMigrated=2
eq_nobargain
Extended Cox Model
Hazard Function
Log Normal Models
LRT Statistic
LTRC Data
Martingale Residuals
nonparametric survival analysis
PH Assumption
PH Model
Quantile Function
Regression Quantile
Regression Quantile Model
Schoenfeld Residuals
Weibull Regression Model
Product details
- ISBN 9781584884088
- Weight: 680g
- Dimensions: 156 x 234mm
- Publication Date: 28 Jul 2003
- Publisher: Taylor & Francis Inc
- Publication City/Country: US
- Product Form: Hardback
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Survival 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.
