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Table of Contents
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
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