Statistical modelling of survival data with random effects : h-likelihood approach
著者
書誌事項
Statistical modelling of survival data with random effects : h-likelihood approach
（Statistics for biology and health）
Springer, c2017
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注記
Includes bibliographical references (p. 265-277) and index
内容説明・目次
内容説明
This book provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood in order to obtain the (marginal) likelihood and to address the computational difficulties in inferences and extensions. The approach presented in the book overcomes shortcomings in the traditional likelihood-based methods for clustered survival data such as intractable integration. The text includes technical materials such as derivations and proofs in each chapter, as well as recently developed software programs in R ("frailtyHL"), while the real-world data examples together with an R package, "frailtyHL" in CRAN, provide readers with useful hands-on tools. Reviewing new developments since the introduction of the h-likelihood to survival analysis (methods for interval estimation of the individual frailty and for variable selection of the fixed effects in the general class of frailty models) and guiding future directions, the book is of interest to researchers in medical and genetics fields, graduate students, and PhD (bio) statisticians.
目次
Chapter 1: Introduction.- 1.1: Goals.- 1.2: Motivating Examples.- 1.2.1: Kidney Infection Data.- 1.2.2: Litter-Matched Rat Data.- 1.2.3: CGD recurrent Data.- 1.2.4: Bladder Cancer Multi-Center Data.- 1.2.5: Lung Cancer Multi-Center Data.- 1.2.6: Breast Cancer Competing Risks Data.- 1.3: Classical Survival Analysis.- 1.3.1: Hazard and Survival Function.- 1.3.2: Basic Likelihood Inference.- 1.3.3: Cox-PH Models.- 1.3.4: Accelerated Failure Time Models.- 1.4: Overview.- Chapter 2: H-likelihood.- 2.1: Definition of H-likelihood.- 2.2: Random Effect Models.- 2.3: Inferential Procedures.- 2.4: Deviances based on H-likelihood.- 2.5: Comparison of H-and Marginal likelihoods.- 2.6: Discussion and Further Reading.- 2.7: Appendix.- Chapter 3: Simple Frailty Models.- 3.1: Features of Correlated Survival Data.- 3.2: The Model and H-likelihood.- 3.3: Inferential Procedures using R.- 3.4: Interval estimation of Frailty.- 3.5: Variable Selection.- 3.6: Discussion and Further Reading.- 3.7: Appendix.- Chapter 4: Multi-Component Frailty Models.- 4.1: Multi-Component Frailty Models.- 4.1.1: Multilevel (Nested) Frailties.- 4.1.2: Time-Dependent Frailties.- 4.1.3: Correlated Frailties.- 4.2: Extension of Inferential Procedures.- 4.3: Model Selection.- 4.4: Software and Examples using R.- 4.5: Discussion and Further Reading.- 4.6: Appendix.- Chapter 5: Competing Risks Frailty Models.- 5.1: Features of Competing Risks Data.- 5.2: Classical Competing-Risk Models.- 5.3: Cause-Specific Hazard Frailty Models.- 5.4: Subdistribution hazard Frailty Models.- 5.5: Semi-Competing Risks Frailty Models.- 5.6: Software and Examples using R.- 5.7: Discussion and Further Reading.- 5.8: Appendix.- Chapter 6: Mixed-Effect Survival Models.- 6.1: Mixed linear Models with Censoring.- 6.2: Multilevel Mixed Models with Censoring.- 6.3: Genetic Mixed Models under LTRC.- 6.4: Software and Examples using SAS/IML.- 6.5: Discussion and Further Reading.- 6.6: Appendix.- Chapter 7: Special Topics.- 7.1: Dispersion Frailty Models.- 7.2: Frailty Models for Interval-Censored Data.- 7.3: Non-PH Frailty Models.- 7.4: Joint Survival Models.- 7.5: Frailty modelling for Missing Cause of Failure.- 7.6: Discussion and Further Reading
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