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Biblioteca

MARC Lista (tabellare)
Annual review of statistics and its application
Annual review of statistics and its application
Pubbl/distr/stampa Palo Alto, California : , : Annual Reviews, , [2014]-
Descrizione fisica 1 online resource
Disciplina 001.4
Soggetto topico Statistics
Statistics - Methodology
Statistics as Topic
Soggetto genere / forma Periodical
Electronic journals.
Periodicals.
Soggetto non controllato Mathematical Statistics
ISSN 2326-831X
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti Statistics and its application
Record Nr. UNISA-996262750303316
Palo Alto, California : , : Annual Reviews, , [2014]-
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Annual review of statistics and its application
Annual review of statistics and its application
Pubbl/distr/stampa Palo Alto, California : , : Annual Reviews, , [2014]-
Descrizione fisica 1 online resource
Disciplina 001.4
Soggetto topico Statistics
Statistics - Methodology
Statistics as Topic
Soggetto genere / forma Periodical
Periodicals.
Soggetto non controllato Mathematical Statistics
ISSN 2326-831X
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Altri titoli varianti Statistics and its application
Record Nr. UNINA-9910282158903321
Palo Alto, California : , : Annual Reviews, , [2014]-
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Calcutta Statistical Association bulletin
Calcutta Statistical Association bulletin
Pubbl/distr/stampa Calcutta, : H. Chatterjee & Co., [1947-
Descrizione fisica 1 online resource
Soggetto topico Statistics - Methodology
Statistics as Topic
Soggetto genere / forma Periodical
ISSN 2456-6462
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Record Nr. UNISA-996251336403316
Calcutta, : H. Chatterjee & Co., [1947-
Materiale a stampa
Lo trovi qui: Univ. di Salerno
Opac: Controlla la disponibilità qui
Calcutta Statistical Association bulletin
Calcutta Statistical Association bulletin
Pubbl/distr/stampa Calcutta, : H. Chatterjee & Co., [1947-
Descrizione fisica 1 online resource
Soggetto topico Statistics - Methodology
Statistics as Topic
Soggetto genere / forma Periodical
ISSN 2456-6462
Formato Materiale a stampa
Livello bibliografico Periodico
Lingua di pubblicazione eng
Record Nr. UNINA-9910446842403321
Calcutta, : H. Chatterjee & Co., [1947-
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Causality in a social world : moderation, meditation and spill-over / / Guanglei Hong
Causality in a social world : moderation, meditation and spill-over / / Guanglei Hong
Autore Hong Guanglei
Pubbl/distr/stampa Chichester, England : , : Wiley, , 2015
Descrizione fisica 1 online resource (1031 p.)
Disciplina 519.5
Soggetto topico Mathematical statistics
Research - Methodology
Statistics - Methodology
ISBN 1-119-03063-3
1-119-03060-9
1-119-03064-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Table of Contents; Title page; Preface; Part I: OVERVIEW; 1 Introduction; 1.1 Concepts of moderation, mediation, and spill-over; 1.2 Weighting methods for causal inference; 1.3 Objectives and organization of the book; 1.4 How is this book situated among other publications on related topics?; References; 2 Review of causal inference concepts and methods; 2.1 Causal inference theory; 2.2 Applications to Lord's paradox and Simpson's paradox; 2.3 Identification and estimation; Appendix 2.1: Potential bias in a prima facie effect
Appendix 2.2: Application of the causal inference theory to Lord's paradox References; 3 Review of causal inference designs and analytic methods; 3.1 Experimental designs; 3.2 Quasi-experimental designs; 3.3 Statistical adjustment methods; 3.4 Propensity score; Appendix 3.A: Potential bias due to the omission of treatment-by-covariate interaction; Appendix 3.B: Variable selection for the propensity score model; References; 4 Adjustment for selection bias through weighting; 4.1 Weighted estimation of population parameters in survey sampling
4.2 Weighting adjustment for selection bias in causal inference 4.3 MMWS; Appendix 4.A: Proof of MMWS-adjusted mean observed outcome being unbiased for the population average potential outcome; Appendix 4.B: Derivation of MMWS for estimating the treatment effect on the treated; Appendix 4.C: Theoretical equivalence of MMWS and IPTW; Appendix 4.D: Simulations comparing MMWS and IPTW under misspecifications of the functional form of a propensity score model; References; 5 Evaluations of multivalued treatments; 5.1 Defining the causal effects of multivalued treatments
5.2 Existing designs and analytic methods for evaluating multivalued treatments 5.3 MMWS for evaluating multivalued treatments; 5.4 Summary; Appendix 5.A: Multiple IV for evaluating multivalued treatments; References; Part II: MODERATION; 6 Moderated treatment effects: concepts and existing analytic methods; 6.1 What is moderation?; 6.2 Experimental designs and analytic methods for investigating explicit moderators; 6.3 Existing research designs and analytic methods for investigating implicit moderators
Appendix 6.A: Derivation of bias in the fixed-effects estimator when the treatment effect is heterogeneous in multisite randomized trials Appendix 6.B: Derivation of bias in the mixed-effects estimator when the probability of treatment assignment varies across sites; Appendix 6.C: Derivation and proof of the population weight applied to mixed-effects models for eliminating bias in multisite randomized trials; References; 7 Marginal mean weighting through stratification for investigating moderated treatment effects; 7.1 Existing methods for moderation analyses with quasi-experimental data
7.2 MMWS estimation of treatment effects moderated by individual or contextual characteristics
Record Nr. UNINA-9910166637503321
Hong Guanglei  
Chichester, England : , : Wiley, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Causality in a social world : moderation, meditation and spill-over / / Guanglei Hong
Causality in a social world : moderation, meditation and spill-over / / Guanglei Hong
Autore Hong Guanglei
Pubbl/distr/stampa Chichester, England : , : Wiley, , 2015
Descrizione fisica 1 online resource (1031 p.)
Disciplina 519.5
Soggetto topico Mathematical statistics
Research - Methodology
Statistics - Methodology
ISBN 1-119-03063-3
1-119-03060-9
1-119-03064-1
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Nota di contenuto Cover; Table of Contents; Title page; Preface; Part I: OVERVIEW; 1 Introduction; 1.1 Concepts of moderation, mediation, and spill-over; 1.2 Weighting methods for causal inference; 1.3 Objectives and organization of the book; 1.4 How is this book situated among other publications on related topics?; References; 2 Review of causal inference concepts and methods; 2.1 Causal inference theory; 2.2 Applications to Lord's paradox and Simpson's paradox; 2.3 Identification and estimation; Appendix 2.1: Potential bias in a prima facie effect
Appendix 2.2: Application of the causal inference theory to Lord's paradox References; 3 Review of causal inference designs and analytic methods; 3.1 Experimental designs; 3.2 Quasi-experimental designs; 3.3 Statistical adjustment methods; 3.4 Propensity score; Appendix 3.A: Potential bias due to the omission of treatment-by-covariate interaction; Appendix 3.B: Variable selection for the propensity score model; References; 4 Adjustment for selection bias through weighting; 4.1 Weighted estimation of population parameters in survey sampling
4.2 Weighting adjustment for selection bias in causal inference 4.3 MMWS; Appendix 4.A: Proof of MMWS-adjusted mean observed outcome being unbiased for the population average potential outcome; Appendix 4.B: Derivation of MMWS for estimating the treatment effect on the treated; Appendix 4.C: Theoretical equivalence of MMWS and IPTW; Appendix 4.D: Simulations comparing MMWS and IPTW under misspecifications of the functional form of a propensity score model; References; 5 Evaluations of multivalued treatments; 5.1 Defining the causal effects of multivalued treatments
5.2 Existing designs and analytic methods for evaluating multivalued treatments 5.3 MMWS for evaluating multivalued treatments; 5.4 Summary; Appendix 5.A: Multiple IV for evaluating multivalued treatments; References; Part II: MODERATION; 6 Moderated treatment effects: concepts and existing analytic methods; 6.1 What is moderation?; 6.2 Experimental designs and analytic methods for investigating explicit moderators; 6.3 Existing research designs and analytic methods for investigating implicit moderators
Appendix 6.A: Derivation of bias in the fixed-effects estimator when the treatment effect is heterogeneous in multisite randomized trials Appendix 6.B: Derivation of bias in the mixed-effects estimator when the probability of treatment assignment varies across sites; Appendix 6.C: Derivation and proof of the population weight applied to mixed-effects models for eliminating bias in multisite randomized trials; References; 7 Marginal mean weighting through stratification for investigating moderated treatment effects; 7.1 Existing methods for moderation analyses with quasi-experimental data
7.2 MMWS estimation of treatment effects moderated by individual or contextual characteristics
Record Nr. UNINA-9910812455703321
Hong Guanglei  
Chichester, England : , : Wiley, , 2015
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Designing a system for collecting policy-relevant data for the Kurdistan Region-Iraq / / Sandra H. Berry [and ten others]
Designing a system for collecting policy-relevant data for the Kurdistan Region-Iraq / / Sandra H. Berry [and ten others]
Autore Berry Sandra H. <1949->
Pubbl/distr/stampa Santa Monica, California : , : RAND Corporation, , 2014
Descrizione fisica 1 online resource (132 pages)
Disciplina 001.422
Soggetto topico Statistics - Methodology
ISBN 0-8330-8646-4
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNINA-9910220154703321
Berry Sandra H. <1949->  
Santa Monica, California : , : RAND Corporation, , 2014
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman
The elements of statistical learning : data mining, inference, and prediction / Trevor Hastie, Robert Tibshirani, Jerome Friedman
Autore Hastie, Trevor
Edizione [2nd ed.]
Pubbl/distr/stampa New York, NY : Springer, c2009
Descrizione fisica XXII, 745 p. : ill. (some col.) ; 25 cm
Disciplina 006.31
Altri autori (Persone) Tibshirani, Robertauthor
Friedman, Jerome H.author
Collana Springer series in statistics, 0172-7397
Soggetto topico Machine learning
Statistics - Methodology
Data mining
Macchine - Apprendimento - Metodi statistici
ISBN 9780387848570
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
Record Nr. UNISALENTO-991003580749707536
Hastie, Trevor  
New York, NY : Springer, c2009
Materiale a stampa
Lo trovi qui: Univ. del Salento
Opac: Controlla la disponibilità qui
Emerging topics in modeling interval-censored survival data / / Jianguo Sun, Ding-Geng Chen, editors
Emerging topics in modeling interval-censored survival data / / Jianguo Sun, Ding-Geng Chen, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (322 pages)
Disciplina 519.52
Collana ICSA book series in statistics
Soggetto topico Censored observations (Statistics)
Statistics - Methodology
Estadística
Metodologia
Soggetto genere / forma Llibres electrònics
ISBN 3-031-12366-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
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.
Record Nr. UNINA-9910633918703321
Cham, Switzerland : , : Springer, , [2022]
Materiale a stampa
Lo trovi qui: Univ. Federico II
Opac: Controlla la disponibilità qui
Emerging topics in modeling interval-censored survival data / / Jianguo Sun, Ding-Geng Chen, editors
Emerging topics in modeling interval-censored survival data / / Jianguo Sun, Ding-Geng Chen, editors
Pubbl/distr/stampa Cham, Switzerland : , : Springer, , [2022]
Descrizione fisica 1 online resource (322 pages)
Disciplina 519.52
Collana ICSA book series in statistics
Soggetto topico Censored observations (Statistics)
Statistics - Methodology
Estadística
Metodologia
Soggetto genere / forma Llibres electrònics
ISBN 3-031-12366-2
Formato Materiale a stampa
Livello bibliografico Monografia
Lingua di pubblicazione eng
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.
Record Nr. UNISA-996499868103316
Cham, Switzerland : , : Springer, , [2022]
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