LEADER 05166oam 2200613 450 001 9910829905403321 005 20230607230035.0 010 $a1-282-24269-5 010 $a9786613813817 010 $a1-118-03298-5 010 $a1-118-03123-7 035 $a(CKB)2560000000055352 035 $a(MiAaPQ)EBC708259 035 $a(EXLCZ)992560000000055352 100 $a20020627d2002 uy 0 101 0 $aeng 135 $aur|n#|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 14$aThe statistical analysis of failure time data /$fJohn D. Kalbfleisch, Ross L. Prentice 205 $aSecond edition. 210 1$aHoboken, N.J. :$cJ. Wiley,$d[2002] 215 $a1 online resource (464 pages) $cillustrations 225 1 $aWiley series in probability and statistics 300 $aDescription based upon print version of record 311 08$aPrint version: 9780471363576 311 08$a0-471-36357-X 320 $aIncludes bibliographical references (pages 404-427) and indexes 327 $aThe Statistical Analysis of Failure Time Data; Contents; Preface; 1. Introduction; 1.1 Failure Time Data; 1.2 Failure Time Distributions; 1.3 Time Origins, Censoring, and Truncation; 1.4 Estimation of the Survivor Function; 1.5 Comparison of Survival Curves; 1.6 Generalizations to Accommodate Delayed Entry; 1.7 Counting Process Notation; Bibliographic Notes; Exercises and Complements; 2. Failure Time Models; 2.1 Introduction; 2.2 Some Continuous Parametric Failure Time Models; 2.3 Regression Models; 2.4 Discrete Failure Time Models; Bibliographic Notes; Exercises and Complements 327 $a3. Inference in Parametric Models and Related Topics3.1 Introduction; 3.2 Censoring Mechanisms; 3.3 Censored Samples from an Exponential Distribution; 3.4 Large-Sample Likelihood Theory; 3.5 Exponential Regression; 3.6 Estimation in Log-Linear Regression Models; 3.7 Illustrations in More Complex Data Sets; 3.8 Discrimination Among Parametric Models; 3.9 Inference with Interval Censoring; 3.10 Discussion; Bibliographic Notes; Exercises and Complements; 4. Relative Risk (Cox) Regression Models; 4.1 Introduction; 4.2 Estimation of ?; 4.3 Estimation of the Baseline Hazard or Survivor Function 327 $a4.4 Inclusion of Strata4.5 Illustrations; 4.6 Counting Process Formulas; 4.7 Related Topics on the Cox Model; 4.8 Sampling from Discrete Models; Bibliographic Notes; Exercises and Complements; 5. Counting Processes and Asymptotic Theory; 5.1 Introduction; 5.2 Counting Processes and Intensity Functions; 5.3 Martingales; 5.4 Vector-Valued Martingales; 5.5 Martingale Central Limit Theorem; 5.6 Asymptotics Associated with Chapter 1; 5.7 Asymptotic Results for the Cox Model; 5.8 Asymptotic Results for Parametric Models; 5.9 Efficiency of the Cox Model Estimator; 5.10 Partial Likelihood Filtration 327 $aBibliographic NotesExercises and Complements; 6. Likelihood Construction and Further Results; 6.1 Introduction; 6.2 Likelihood Construction in Parametric Models; 6.3 Time-Dependent Covariates and Further Remarks on Likelihood Construction; 6.4 Time Dependence in the Relative Risk Model; 6.5 Nonnested Conditioning Events; 6.6 Residuals and Model Checking for the Cox Model; Bibliographic Notes; Exercises and Complements; 7. Rank Regression and the Accelerated Failure Time Model; 7.1 Introduction; 7.2 Linear Rank Tests; 7.3 Development and Properties of Linear Rank Tests 327 $a7.4 Estimation in the Accelerated Failure Time Model7.5 Some Related Regression Models; Bibliographic Notes; Exercises and Complements; 8. Competing Risks and Multistate Models; 8.1 Introduction; 8.2 Competing Risks; 8.3 Life-History Processes; Bibliographic Notes; Exercises and Complements; 9. Modeling and Analysis of Recurrent Event Data; 9.1 Introduction; 9.2 Intensity Processes for Recurrent Events; 9.3 Overall Intensity Process Modeling and Estimation; 9.4 Mean Process Modeling and Estimation; 9.5 Conditioning on Aspects of the Counting Process History; Bibliographic Notes 327 $aExercises and Complements 330 $aContains additional discussion and examples on left truncation as well as material on more general censoring and truncation patterns.Introduces the martingale and counting process formulation swil lbe in a new chapter.Develops multivariate failure time data in a separate chapter and extends the material on Markov and semi Markov formulations.Presents new examples and applications of data analysis 410 0$aWiley series in probability and statistics. 606 $aFailure time data analysis 606 $aSurvival analysis (Biometry) 606 $aRegression analysis 615 0$aFailure time data analysis. 615 0$aSurvival analysis (Biometry) 615 0$aRegression analysis. 676 $a519.287 676 $a519.5 700 $aKalbfleisch$b J. D.$0128130 702 $aPrentice$b Ross L. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910829905403321 996 $aStatistical analysis of failure time data$9198824 997 $aUNINA