Analysis of Survival DataThis monograph contains many ideas on the analysis of survival data to present a comprehensive account of the field. The value of survival analysis is not confined to medical statistics, where the benefit of the analysis of data on such factors as life expectancy and duration of periods of freedom from symptoms of a disease as related to a treatment applied individual histories and so on, is obvious. The techniques also find important applications in industrial life testing and a range of subjects from physics to econometrics. In the eleven chapters of the book the methods and applications of are discussed and illustrated by examples. |
Contents
| 1 | |
Distributions of failure time | 13 |
Parametric statistical analysis single sample | 32 |
Singlesample nonparametric methods | 48 |
Dependence on explanatory variables model formulation | 62 |
Fully parametric analysis of dependency | 80 |
Proportional hazards model | 91 |
Timedependent covariates | 112 |
Several types of failure | 142 |
Bivariate survivor functions | 156 |
Selfconsistency and the EM algorithm | 165 |
References | 183 |
| 195 | |
| 198 | |
Other editions - View all
Common terms and phrases
algorithm analysis applications approximate asymptotic Biometrika bivariate calculation censored data censoring Chapter component computation conditional consider constant continuous corresponding covariates defined density depend derived discussion effect equation examine example Exercise expectation explanatory variables exponential distribution fail fixed follows further gamma given gives groups hazard function independent individuals integral interest interpretation interval introduced involving joint limit linear log likelihood logistic matrix maximum likelihood estimator mean measured methods needed nonparametric normal Note observed obtained parameter particular patients plots possible probability problem procedures processes properties proportional hazards model random variable rank ratio regression relative represent require respectively risk risk set sample Show simple single specified standard Statist Suppose survival survivor function Table theory time-dependent transplant treatment types of failure usually values variance vector Weibull