Statistical Models and Methods for Lifetime DataPraise for the First Edition "An indispensable addition to any serious collection on lifetime data analysis and . . . a valuable contribution to the statistical literature. Highly recommended . . ." -Choice "This is an important book, which will appeal to statisticians working on survival analysis problems." -Biometrics "A thorough, unified treatment of statistical models and methods used in the analysis of lifetime data . . . this is a highly competent and agreeable statistical textbook." -Statistics in Medicine The statistical analysis of lifetime or response time data is a key tool in engineering, medicine, and many other scientific and technological areas. This book provides a unified treatment of the models and statistical methods used to analyze lifetime data. Equally useful as a reference for individuals interested in the analysis of lifetime data and as a text for advanced students, Statistical Models and Methods for Lifetime Data, Second Edition provides broad coverage of the area without concentrating on any single field of application. Extensive illustrations and examples drawn from engineering and the biomedical sciences provide readers with a clear understanding of key concepts. New and expanded coverage in this edition includes: * Observation schemes for lifetime data * Multiple failure modes * Counting process-martingale tools * Both special lifetime data and general optimization software * Mixture models * Treatment of interval-censored and truncated data * Multivariate lifetimes and event history models * Resampling and simulation methodology |
Contents
| 1 | |
2 Observation Schemes Censoring and Likelihood | 49 |
3 Some Nonparametric and Graphical Procedures | 79 |
4 Inference Procedures for Parametric Models | 147 |
5 Inference Procedures for LogLocationScale Distributions | 211 |
6 Parametric Regression Models | 269 |
7 Semiparametric Multiplicative Hazards Regression Models | 341 |
8 RankType and Other Semiparametric Procedures for LogLocationScale Models | 401 |
9 Multiple Modes of Failure | 433 |
10 GoodnessofFit Tests | 465 |
11 Beyond Univariate Survival Analysis | 491 |
Appendices | 535 |
References | 577 |
| 611 | |
| 621 | |
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Common terms and phrases
95 confidence interval analysis Appendix approach approximate assumed bootstrap censored data censored random sample Chapter confidence intervals confidence limits consider covariance matrix cumulative hazard function defined discussed in Section exacerbation Example exponential distribution extreme value distribution find first fit fitted fixed gamma gamma distribution given gives hazard function hypothesis independent individuals inference information matrix Kalbfleisch Kaplan—Meier estimate large-sample lifetime data lifetime distribution likelihood function likelihood ratio statistic linear location-scale models log rank log-Burr log-likelihood function log-logistic log-normal maximized maximum likelihood methods nonparametric estimates Note observed obtained p-value parametric models PH model pivotal quantity Pr(T probability plots Problem procedures profile quantiles random variables rank tests regression models residuals S-Plus scale parameter score semiparametric shows significant simulation specific survival survivor function Table tests based Theorem time-varying covariates tion treatment truncation Type 2 censored uncensored variance estimate vector Weibull distribution Weibull model