The statistical analysis of failure time data
* Contains additional discussion and examples on left truncation as well as material on more general censoring and truncation patterns.
* Introduces the martingale and counting process formulation swil lbe in a new chapter.
* Develops multivariate failure time data in a separate chapter and extends the material on Markov and semi Markov formulations.
* Presents new examples and applications of data analysis.
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Failure Time Models
Inference in Parametric Models and Related Topics
Relative Risk Cox Regression Models
12 other sections not shown
accelerated failure analysis Ao(f apply approximate arise assumption asymptotic distribution asymptotic results asymptotic variance basic covariates censored data censoring censorship Chapter competing risks condition consider continuous converges corresponding counting process Cox model cumulative hazard function defined density depend derived Dirichlet process efficiency example exponential distribution failure rate failure time data failure time model failure types gamma gamma distribution given gives hazard function independent censoring intensity ith individual Kalbfleisch Kaplan-Meier estimator leukemia likelihood function linear log-rank test marginal marginal likelihood martingale matrix maximum likelihood estimate methods Nelson-Aalen estimator nonparametric Note number of failures obtained parametric models partial likelihood patients Prentice probability procedure proportional hazards model random rank tests regression model regression parameter relative risk model residuals right censoring risk set sample score statistic specified study subjects Suppose survival survivor function survivor function estimators Table time-dependent covariates tion transplant treatment uncensored variance estimator Wilcoxon