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© 2017

Statistical Modelling of Survival Data with Random Effects

H-Likelihood Approach

Benefits

  • Provides a groundbreaking introduction to the likelihood inference for correlated survival data via the hierarchical (or h-) likelihood

  • Includes R package, “frailtyHL” in CRAN, to fit various frailty models

  • Reviews state-of-the-art statistical methods in likelihood theory and application

Book

Part of the Statistics for Biology and Health book series (SBH)

Table of contents

  1. Front Matter
    Pages i-xiv
  2. Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
    Pages 1-5
  3. Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
    Pages 7-36
  4. Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
    Pages 37-65
  5. Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
    Pages 67-104
  6. Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
    Pages 105-123
  7. Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
    Pages 125-171
  8. Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
    Pages 173-197
  9. Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
    Pages 199-227
  10. Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
    Pages 229-243
  11. Il Do Ha, Jong-Hyeon Jeong, Youngjo Lee
    Pages 245-260
  12. Back Matter
    Pages 261-283

About this book

Introduction

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.      

Keywords

Accelerated Failure Time Models Basic Likelihood Inference Classical Survival Analysis in Statistics Comparison of H-and Marginal likelihoods Correlated Frailties Correlated Survival Data Cox-PH Models Dispersion Frailty Models Extension of Inferential Procedures Frailty Models for Interval-Censored Data Frailty modelling for Missing Cause of Failure Genetic Mixed Models under LTRC Hazard and Survival Function Joint Survival Models Mixed linear Models with Censoring Mixed-Effect Survival Models Multi-Component Frailty Models Multilevel (Nested) Frailties Multilevel Mixed Models with Censoring Non-PH Frailty Models

Authors and affiliations

  1. 1.Department of StatisticsPukyong National UniversityBusanKorea (Republic of)
  2. 2.Department of BiostatisticsUniversity of PittsburghPittsburghUSA
  3. 3.Department of StatisticsSeoul National UniversitySeoulKorea (Republic of)

About the authors

Il Do Ha is a full professor in the Department of Statistics at Pukyong National University in South Korea. His research interests are multivariate survival analysis using h-likelihood, inferences on random-effect models, clinical trials and financial statistics. Dr. Ha received his Ph.D. degree in statistics from Seoul National University. He has served as an Associate Editor of Computational Statistics until 2008-2012 and has been a fellow of the Royal Statistical Society (RSS) since 2006. Jong-Hyeon Jeong is a full professor in the Department of Biostatistics at University of Pittsburgh in USA. His research interests are in survival analysis, including competing risks, quantile residual life, empirical likelihood, h-likelihood, frailty model and clinical trials. He has published his first book with Springer: Jeong, J.-H. (2014) Statistical Inference on Residual Life, New York: Springer. Dr. Jeong received his Ph.D. degree in statistics from University of Rochester. He has been a fellow of the American Statistical Association (ASA) since 2017 as well as an elected member of the international Statistical Institute (ISI) since 2007. Dr. Jeong is also serving on the editorial board for the journal “Lifetime Data Analysis”. Youngjo Lee is a full professor in the Department of Statistics at Seoul National University in South Korea and also an adjunct professor of Karolinska Institutet in Sweden. His research interests are extension, application, theory and software development for hierarchical GLM (HGLM) and multivariate survival models using h-likelihood. He has published a HGLM book with Chapman and Hall: Lee, Y., Nelder, J. A. and Pawitan, Y. (2017) Generalized Linear Models with Random Effects: Unified Analysis via H-likelihood, 2nd edition, Boca Raton: Chapman and Hall. Dr. Lee received his Ph.D. degree in statistics from Iowa State University. He has been a fellow of the Royal Statistical Society (RSS) since 1996 as well as the American Statistical Association (ASA) since 2013.

Bibliographic information

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