Bayesian Biostatistics

Emmanuel Lesaffre 지음

Wiley 2012년 06월 18일 출간

작품소개

책소개

The growth of biostatistics has been phenomenal in recent years and has been marked by considerable technical innovation in both methodology and computational practicality. One area that has experienced significant growth is Bayesian methods. The growing use of Bayesian methodology has taken place partly due to an increasing number of practitioners valuing the Bayesian paradigm as matching that of scientific discovery. In addition, computational advances have allowed for more complex models to be fitted routinely to realistic data sets.Through examples, exercises and a combination of introductory and more advanced chapters, this book provides an invaluable understanding of the complex world of biomedical statistics illustrated via a diverse range of applications taken from epidemiology, exploratory clinical studies, health promotion studies, image analysis and clinical trials.Key Features:Provides an authoritative account of Bayesian methodology, from its most basic elements to its practical implementation, with an emphasis on healthcare techniques.Contains introductory explanations of Bayesian principles common to all areas of application.Presents clear and concise examples in biostatistics applications such as clinical trials, longitudinal studies, bioassay, survival, image analysis and bioinformatics.Illustrated throughout with examples using software including WinBUGS, OpenBUGS, SAS and various dedicated R programs.Highlights the differences between the Bayesian and classical approaches.Supported by an accompanying websitehosting free software and case study guides.Bayesian Biostatistics introduces the reader smoothly into the Bayesian statistical methods with chapters that gradually increase in level of complexity. Master students in biostatistics, applied statisticians and all researchers with a good background in classical statistics who have interest in Bayesian methods will fin

목차

Preface xiiiNotation, terminology and some guidance for reading the book xviiPart I BASIC CONCEPTS IN BAYESIAN METHODS1 Modes of statistical inference 31.1 The frequentist approach: A critical reflection 41.2 Statistical inference based on the likelihood function 101.3 The Bayesian approach: Some basic ideas 141.4 Outlook 182 Bayes theorem: Computing the posterior distribution 202.1 Introduction 202.2 Bayes theorem &n- the binary version 202.3 Probability in a Bayesian context 212.4 Bayes theorem &n- the categorical version 222.5 Bayes theorem &n- the continuous version 232.6 The binomial case 242.7 The Gaussian case 302.8 The Poisson case 362.9 The prior and posterior distribution of h(θ) 402.10 Bayesian versus likelihood approach 402.11 Bayesian versus frequentist approach 412.12 The different modes of the Bayesian approach 412.13 An historical note on the Bayesian approach 422.14 Closing remarks 443 Introduction to Bayesian inference 463.1 Introduction 463.2 Summarizing the posterior by probabilities 463.3 Posterior summary measures 473.4 Predictive distributions 513.5 Exchangeability 583.6 A normal approximation to the posterior 603.7 Numerical techniques to determine the posterior 633.8 Bayesian hypothesis testing 723.9 Closing remarks 784 More than one parameter 824.1 Introduction 824.2 Joint versus marginal posterior inference 834.3 The normal distribution with μ and σ2 unknown 834.4 Multivariate distributions 894.5 Frequentist properties of Bayesian inference 924.6 Sampling from the posterior distribution: The Method of Composition 934.7 Bayesian linear regression models 964.8 Bayesian generalized linear models 1014.9 More complex regression models

출판사 서평

“In conclusion, we consider the book by Lesaffre and Lawson a noteworthy contribution to the dissemination of Bayesian methods, and a good manual of reference for many common and some specialized applications in biomedical research. The great variety of examples and topics covered offers both advantages and disadvantages. Some parts might be too specialized for statistics students, but lecturers and applied statisticians will benefit a lot from the authors&' wealth of experience.”& (Biometrical Journal, 15 July 2013)“The book Bayesian Biostatisticsby Lesaffre and Lawson, is a welcoming addition to this important area of research in biostatistical applications. For example, in the area of clinical trials, Bayesian methods provide flexibility and benefits for incorporating historical data with current data and then using the resulting posterior to make probability statements for different outcomes”.(Journal of Biopharmaceutical Statistics, 1 January 2013)&

작가정보

저자(글) Emmanuel Lesaffre

저자 :

저자 :

저자 :

저자 :

저자 :
저자 :
저자 :
Emmanuel Lesaffre, Professor of Statistics, Biostatistical Centre, Catholic University of Leuven, Leuven, Belgium. Dr Lesaffre has worked on and studied various areas of biostatistics for 25 years. He has taught a variety of courses to students from many disciplines, from medicine and pharmacy, to statistics and engineering, teaching Bayesian statistics for the last 5 years. Having published over 200 papers in major statistical and medical journals, he has also Co-Edited the book Disease Mapping and Risk Assessment for Public Health, and was the Associate Editor for Biometrics. He is currently Co-Editor of the journal ldquo;Statistical Modelling: An International Journalrdquo;, Special Editor of two volumes on Statistics in Dentistry in Statistical Methods in Medical Research, and a member of the Editorial Boards of numerous journals.Andrew Lawson, Professor of Statistics, Dept of Epidemiology Biostatistics, University of South Carolina, USA. Dr Lawson has considerable and wide ranging experience in the development of statistical methods for spatial and environmental epidemiology. He has solid experience in teaching Bayesian statistics to students studying biostatistics and has also written two books and numerous journal articles in the biostatistics area. Dr Lawson has also guest edited two special issues of ldquo;Statistics in Medicinerdquo; focusing on Disease Mapping. He is a member of the editorial boards of the journals: Statistics in Medicine and .