Statistical Modelling of Survival Data with Random Effects H-Likelih
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
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Il Do Ha Jong-Hyeon Jeong Youngjo Lee
Statistical Modelling of Survival Data with Random Effects H-Likelihood Approach
Statistics for Biology and Health Series Editors Mitchell Gail Jonathan M. Samet B. Singer Anastasios Tsiatis
More information about this series at http://www.springer.com/series/2848
Il Do Ha Jong-Hyeon Jeong Youngjo Lee •
Statistical Modelling of Survival Data with Random Effects H-Likelihood Approach
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Il Do Ha Department of Statistics Pukyong National University Busan Korea (Republic of)
Youngjo Lee Department of Statistics Seoul National University Seoul Korea (Republic of)
Jong-Hyeon Jeong Department of Biostatistics University of Pittsburgh Pittsburgh, PA USA
ISSN 1431-8776 ISSN 2197-5671 (electronic) Statistics for Biology and Health ISBN 978-981-10-6555-2 ISBN 978-981-10-6557-6 (eBook) https://doi.org/10.1007/978-981-10-6557-6 Library of Congress Control Number: 2017956741 © Springer Nature Singapore Pte Ltd. 2017 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, express or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. Printed on acid-free paper This Springer imprint is published by Springer Nature The registered company is Springer Nature Singapore Pte Ltd. The registered company address is: 152 Beach Road, #21-01/04 Gateway East, Singapore 189721, Singapore
Preface
Survival or time-to-event data arise in various research areas such as medicine, epidemiology, genetics, engineering, econometrics, and sociology. Survival data have unique features including incomplete observation such as censoring and/or truncation. Use of semi-parametric models and potential correlation among time-to-events from the same cluster can make the statistical inference further complicated. Broad classes of multivariate models using random effects have been developed. For inferences about unobserved random variables, the hierarchical (or h-)likelihood has been proposed by Lee and Nelder (1996). This bo
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