Emerging topics in modeling interval-censored survival data / / Jianguo Sun, Ding-Geng Chen, editors

• Pubblicazione: Cham, Switzerland : , : Springer, , [2022]; ©2022
• Descrizione fisica: 1 online resource (322 pages)
• Disciplina: 519.52
• Soggetto topico: Censored observations (Statistics); Statistics - Methodology; Estadística; Metodologia
• Soggetto genere / forma: Llibres electrònics
• Persona (resp. second.): ChenDing-Geng; SunJianguo <1961->
• Note generali: Includes index.
• Nota di contenuto: Intro -- Preface -- Part I: Introduction and Review (Chapters 1 -3) -- Part II: Emerging Topics in Methodology (Chapters 4 -9) -- Part III: Emerging Topics in Applications (Chapters 10 -15) -- Contents -- Editors and Contributors -- About the Editors -- Contributors -- Part I Introduction and Review -- Overview of Historical Developments in Modeling Interval-Censored Survival Data -- 1 Emerging Interval-Censored Data -- 2 Emerging Methods in Analyzing Interval-Censored Data -- 3 More on Emerging Methods in Analyzing Interval-Censored Data -- References -- Overview of Recent Advances on the Analysis of Interval-Censored Failure Time Data -- 1 Introduction -- 2 Regression Analysis of Univariate Interval-Censored Failure Time Data -- 2.1 Regression Analysis with Time-Dependent Covariates -- 2.2 Regression Analysis in the Presence of a Cured Subgroup -- 2.3 Variable Section for Interval-Censored Data -- 3 Regression Analysis with Informative Interval Censoring -- 4 Regression Analysis of Clustered and Multivariate Interval-Censored Data -- 5 Other Topics on Regression Analysis of Interval-Censored Data -- 6 Concluding Remarks -- References -- Predictive Accuracy of Prediction Model for Interval-Censored Data -- 1 Introduction -- 2 Time-dependent AUC -- 2.1 Review of ROC Curve -- 2.2 ROC for Interval Censored Data -- 2.3 Simulation -- 3 Time-Dependent C-index -- 3.1 Review of C-index -- 3.2 C-index for Interval Censored Data -- 3.3 Simulation -- 4 Brier Score -- 4.1 Review of Brier Score -- 4.2 Brier Score for Interval Censored Data -- 4.3 Simulation -- 5 Application to Dementia Dataset -- 6 Concluding Remarks -- Appendix: R code -- References -- Part II Emerging Topics in Methodology -- A Practical Guide to Exact Confidence Intervals for a Distribution of Current Status Data Using the Binomial Approach -- 1 Introduction. ; 2 Current Status Data and Point Estimations -- 2.1 Current Status Data -- 2.2 The R package csci: Current Status Confidence Intervals -- 2.3 Point Estimation for F(t) -- 3 Valid Binomial Approach Confidence Intervals -- 3.1 A Structure of the Valid Confidence Interval for F(t) -- 3.2 A Specific Form of the Functions a(t,n,C) and b(t,n,C) -- 3.3 Choice of mn -- 4 The ABA (Approximate Binomial Approach) Confidence Intervals -- 4.1 The Structure of the ABA Confidence Interval -- 4.2 Choice of m†n -- 4.3 Aesthetic Adjustments -- 5 Simulation Studies -- 5.1 Simulation 1 -- 5.2 Simulation 2 -- 6 Analyzing the Hepatitis A Data in Bulgaria -- 7 Conclusion -- References -- Accelerated Hazards Model and Its Extensions for Interval-Censored Data -- 1 Why Is Accelerated Hazards Model Needed? -- 2 Accelerated Hazards Model with Interval-Censored Data -- 3 Estimation Procedure -- 3.1 Sieve Semiparametric Maximum Likelihood Estimator -- 3.2 Implementation -- 3.3 Choosing the Number of Base Splines -- 4 Large Sample Properties -- 5 Simulation Study -- 6 Example 1: Diabetes Conversion Data -- 7 Extensions of Accelerated Hazards Model -- 7.1 Generalized Accelerated Hazards Model -- 7.2 GAH Mixture Cure Model -- 8 Sieve Maximum Likelihood Estimation for GAHCure Model -- 8.1 Sieve Likelihood -- 8.2 Algorithm -- 8.3 Simulation Results -- 9 Example 2: Smoking Cessation Data -- References -- Maximum Likelihood Estimation of Semiparametric Regression Models with Interval-Censored Data -- 1 Introduction -- 2 Cox Model and Right-Censored Data -- 3 Interval-Censored Data -- 4 Competing Risks -- 5 Multivariate Failure Time Data -- 6 Remarks -- References -- Use of the INLA Approach for the Analysis of Interval-Censored Data -- 1 Introduction -- 2 Approximate Bayesian Inference with INLA -- 2.1 INLA -- 2.2 The R-INLA Package -- 3 Interval Censored Survival Analysis with INLA. ; 3.1 Survival Models as LGMs -- 3.2 Capabilities and Possibilities for Survival Models in INLA -- 4 Examples -- 4.1 Diabetic Nephropathy: Frailty Log-Logistic Model -- 4.1.1 Frailty Log-Logistic Model as a Latent Gaussian Model -- 4.1.2 Using R-INLA for Full Bayesian Inference -- 4.1.3 Results -- 4.2 Epilepsy Drug Efficacy: Non-linear Joint Model with Competing Risks and Interval Censoring -- 4.2.1 Non-linear Joint Model with Competing Risks as a Latent Gaussian Model -- 4.2.2 Results -- 5 Discussion -- Appendix -- References -- Copula Models and Diagnostics for Multivariate Interval-Censored Data -- 1 Introduction -- 2 Notation and Methods -- 2.1 Copula Model for Multivariate Interval-Censored Data -- 2.2 Joint Likelihood for Bivariate Interval-Censored Data -- 2.3 Choice of Marginal Models -- 3 Parameter Estimation -- 3.1 Sieve Likelihood with Bernstein Polynomials -- 4 Goodness-of-Fit Test for Copula Specification -- 4.1 Hypothesis and Test Statistic -- 4.2 Estimation of IR Statistic -- 4.3 Test Procedure -- 5 Simulation Studies -- 5.1 Generating Bivariate Interval-Censored Times -- 5.2 Simulation-I: Parameter Estimation -- 5.3 Simulation-II: Joint Survival Probability Estimation Performance -- 5.4 Simulation III: IR Test Performance -- 6 Real Examples -- 7 Conclusion -- References -- Efficient Estimation of the Additive Risks Model for Interval-Censored Data -- 1 Introduction -- 2 Statistical Model -- 2.1 Notations and Setup -- 2.2 Likelihood -- 3 Estimation -- 3.1 MM Algorithm -- 3.2 Variance Estimation -- 3.3 Complexity Analysis -- 4 Simulation Study -- 5 Application: Breast Cancer Data -- 6 Implementation: R Package MMIntAdd -- 7 Conclusions -- Appendix -- Proof of Theorem 1 -- References -- Part III Emerging Topics in Applications -- Modeling and Analysis of Chronic Disease Processes Under Intermittent Observation -- 1 Introduction. ; 2 Modeling Multistate Disease and Observation Processes -- 2.1 Multistate Models -- 2.2 Joint Models for the Disease and Visit Process -- 3 Partially Specified Models for Marginal Features -- 4 Cox Models with Markers Under Intermittent Observation -- 4.1 Joint Marker-Failure Visit Process Models -- 4.2 Limiting Value of a Cox Model Coefficient Under LOCF -- 4.3 A Bone Marker and Event-Free Survival -- 5 Discussion -- References -- Case-Cohort Studies with Time-Dependent Covariates and Interval-Censored Outcome -- 1 Introduction -- 2 Model Specification -- 2.1 Full Cohort -- 2.2 Case-Cohort -- 3 Simulation -- 4 Hormonal Contraceptive HIV Data -- 5 Discussion -- Supporting Information -- References -- The BivarIntCensored: An R Package for Nonparametric Inference of Bivariate Interval-Censored Data -- 1 Introduction -- 2 Method -- 2.1 Spline-Based Sieve NPMLE -- 2.1.1 Notation -- 2.1.2 Likelihood Function -- 2.1.3 Spline-Based Sieve NPMLE -- 2.2 A Nonparametric Association Test -- 3 Implementation -- 4 BivarIntCensored Package and Its Illustration -- 4.1 Main Functions -- 4.2 Example -- 5 Conclusions -- References -- Joint Modeling for Longitudinal and Interval-Censored Survival Data: Application to IMPI Multi-Center HIV/AIDS Clinical Trial -- 1 Introduction -- 2 Data and Methods -- 2.1 Data Structure -- 2.1.1 Survival Data -- 2.1.2 Longitudinal Data -- 2.2 The Joint Model -- 2.2.1 The Shared Parameter Joint Model -- 2.2.2 The Joint Models for Interval-Censored Data -- 3 Data Analysis -- 3.1 Illustration Using the IMPI Trial Data -- 3.2 Survival Data Analysis with Time-Dependent Covariates -- 3.3 Longitudinal Data Analysis: Linear Mixed-Effects Model -- 3.4 Joint Modeling for Longitudinal CD4 Counts and Interval-Censored Survival Data -- 4 Discussions -- References. ; Regression Analysis with Interval-Censored Covariates. Application to Liquid Chromatography -- 1 Introduction -- 1.1 Interval-Censored Covariates in Regression Models: State of the Art -- 1.2 Outline -- 2 Motivating Data -- 2.1 Notation -- 2.1.1 Single Compounds -- 2.1.2 Sum of Compounds -- 3 Regression Methods Accounting for Limits of Detection and Quantitation -- 3.1 The GEL Method -- 3.2 Extension to the Generalized Linear Model -- 3.2.1 Regression with the Gamma Distribution -- 3.2.2 Logistic Regression -- 3.3 Comments on the Inclusion of Exact Observations -- 3.4 Residuals for Interval-Censored Covariates -- 3.4.1 Residuals for the Linear Model -- 3.4.2 Extension of GEL Residuals to the Generalized Linear Model -- 3.5 Implementation -- 4 Illustration -- 4.1 Linear Regression Model to Model log(Glucose) -- 4.2 Gamma Regression Model to Model Glucose Levels -- 4.3 Logistic Regression Model for Association with Obesity -- 5 Discussion -- References -- Misclassification Simulation Extrapolation Procedure for Interval-Censored Log-Logistic Accelerated Failure Time Model -- 1 Introduction -- 2 Methodology -- 2.1 Interval-Censored (Type II) Survival Data and Log-Logistic AFT Model with Misclassification Matrix -- 2.2 MC-SIMEX -- 3 Monte-Carlo Simulation Study -- 3.1 Simulation Design -- 3.2 Results of Simulation -- 4 Real Data Analysis -- 5 Discussions -- References -- Index.
• Titolo autorizzato: Emerging topics in modeling interval-censored survival data
• ISBN: 3-031-12366-2
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910633918703321
• Lo trovi qui: Univ. Federico II
• Serie: ICSA book series in statistics.