Search Results for bayesian-biostatistics

Master students in biostatistics, applied statisticians and all researchers with a good background in classical statistics who have interest in Bayesian methods will find this book useful.

Author: Emmanuel Lesaffre

Publisher: John Wiley & Sons

ISBN: 9781118314579

Category: Medical

Page: 536

View: 198

DOWNLOAD & READ
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 website hosting 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 find this book useful.
2012-06-18 By Emmanuel Lesaffre

This work provides descriptions, explanations and examples of the Bayesian approach to statistics, demonstrating the utility of Bayesian methods for analyzing real-world problems in the health sciences.

Author: Donald A. Berry

Publisher: CRC Press

ISBN: 1420002880

Category: Mathematics

Page: 704

View: 512

DOWNLOAD & READ
This work provides descriptions, explanations and examples of the Bayesian approach to statistics, demonstrating the utility of Bayesian methods for analyzing real-world problems in the health sciences. The work considers the individual components of Bayesian analysis.;College or university bookstores may order five or more copies at a special student price, available on request from Marcel Dekker, Inc.
1996-02-13 By Donald A. Berry

Elementary Bayesian Biostatistics is an introduction to the modern world of Bayes
procedures for the clinical scientist with a minimal background in biostatistics.
This book offers the practicing biostatistician with no formal training in Bayes ...

Author: Lemuel A. Moye

Publisher: CRC Press

ISBN: 9781584887256

Category: Mathematics

Page: 400

View: 483

DOWNLOAD & READ
Bayesian analyses have made important inroads in modern clinical research due, in part, to the incorporation of the traditional tools of noninformative priors as well as the modern innovations of adaptive randomization and predictive power. Presenting an introductory perspective to modern Bayesian procedures, Elementary Bayesian Biostatistics explo
2016-04-19 By Lemuel A. Moye

Bayesian methods will be employed to design and analyze studies in medical
diagnostics. This chapter describes Bayesian inference by introducing Bayes
theorem, the foundation of the subject. This is followed with a description of the ...

Author: Lyle D. Broemeling

Publisher: CRC Press

ISBN: 1584887680

Category: Mathematics

Page: 216

View: 370

DOWNLOAD & READ
There are numerous advantages to using Bayesian methods in diagnostic medicine, which is why they are employed more and more today in clinical studies. Exploring Bayesian statistics at an introductory level, Bayesian Biostatistics and Diagnostic Medicine illustrates how to apply these methods to solve important problems in medicine and biology. After focusing on the wide range of areas where diagnostic medicine is used, the book introduces Bayesian statistics and the estimation of accuracy by sensitivity, specificity, and positive and negative predictive values for ordinal and continuous diagnostic measurements. The author then discusses patient covariate information and the statistical methods for estimating the agreement among observers. The book also explains the protocol review process for cancer clinical trials, how tumor responses are categorized, how to use WHO and RECIST criteria, and how Bayesian sequential methods are employed to monitor trials and estimate sample sizes. With many tables and figures, this book enables readers to conduct a Bayesian analysis for a large variety of interesting and practical biomedical problems.
2007-07-12 By Lyle D. Broemeling

5,600 Flash Cards. Q & A FlashCards, Ebooks, Textbooks, Courses, Books Simplified as FlashCards by Powell Publications. Very effective study tools especially when you only have a limited amount of time.

Author:

Publisher:

ISBN:

Category:

Page:

View: 276

DOWNLOAD & READ
By

This book is a synthesis of the conferences and debates, providing an overview of Bayesian methods applied to nearly all stages of research and development, from early discovery to portfolio management.

Author: Emmanuel Lesaffre

Publisher: CRC Press

ISBN: 9781351718677

Category: Medical

Page: 516

View: 170

DOWNLOAD & READ
Since the early 2000s, there has been increasing interest within the pharmaceutical industry in the application of Bayesian methods at various stages of the research, development, manufacturing, and health economic evaluation of new health care interventions. In 2010, the first Applied Bayesian Biostatistics conference was held, with the primary objective to stimulate the practical implementation of Bayesian statistics, and to promote the added-value for accelerating the discovery and the delivery of new cures to patients. This book is a synthesis of the conferences and debates, providing an overview of Bayesian methods applied to nearly all stages of research and development, from early discovery to portfolio management. It highlights the value associated with sharing a vision with the regulatory authorities, academia, and pharmaceutical industry, with a view to setting up a common strategy for the appropriate use of Bayesian statistics for the benefit of patients. The book covers: Theory, methods, applications, and computing Bayesian biostatistics for clinical innovative designs Adding value with Real World Evidence Opportunities for rare, orphan diseases, and pediatric development Applied Bayesian biostatistics in manufacturing Decision making and Portfolio management Regulatory perspective and public health policies Statisticians and data scientists involved in the research, development, and approval of new cures will be inspired by the possible applications of Bayesian methods covered in the book. The methods, applications, and computational guidance will enable the reader to apply Bayesian methods in their own pharmaceutical research.
2020-04-15 By Emmanuel Lesaffre

DOI 10.1016/S0169-7161(05)25025-3 Bayesian Biostatistics David B. Dunson
Abstract With the rapid increase in biomedical technology and the accompanying
generation of complex and high-dimensional data sets, Bayesian statistical ...

Author:

Publisher: Elsevier

ISBN: 0080461174

Category: Mathematics

Page: 1062

View: 969

DOWNLOAD & READ
This volume describes how to develop Bayesian thinking, modelling and computation both from philosophical, methodological and application point of view. It further describes parametric and nonparametric Bayesian methods for modelling and how to use modern computational methods to summarize inferences using simulation. The book covers wide range of topics including objective and subjective Bayesian inferences with a variety of applications in modelling categorical, survival, spatial, spatiotemporal, Epidemiological, software reliability, small area and micro array data. The book concludes with a chapter on how to teach Bayesian thoughts to nonstatisticians. Critical thinking on causal effects Objective Bayesian philosophy Nonparametric Bayesian methodology Simulation based computing techniques Bioinformatics and Biostatistics
2005-11-29 By

Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters.

Author: Lyle D. Broemeling

Publisher: CRC Press

ISBN: 9780429948923

Category: Mathematics

Page: 280

View: 740

DOWNLOAD & READ
In many branches of science relevant observations are taken sequentially over time. Bayesian Analysis of Time Series discusses how to use models that explain the probabilistic characteristics of these time series and then utilizes the Bayesian approach to make inferences about their parameters. This is done by taking the prior information and via Bayes theorem implementing Bayesian inferences of estimation, testing hypotheses, and prediction. The methods are demonstrated using both R and WinBUGS. The R package is primarily used to generate observations from a given time series model, while the WinBUGS packages allows one to perform a posterior analysis that provides a way to determine the characteristic of the posterior distribution of the unknown parameters. Features Presents a comprehensive introduction to the Bayesian analysis of time series. Gives many examples over a wide variety of fields including biology, agriculture, business, economics, sociology, and astronomy. Contains numerous exercises at the end of each chapter many of which use R and WinBUGS. Can be used in graduate courses in statistics and biostatistics, but is also appropriate for researchers, practitioners and consulting statisticians. About the author Lyle D. Broemeling, Ph.D., is Director of Broemeling and Associates Inc., and is a consulting biostatistician. He has been involved with academic health science centers for about 20 years and has taught and been a consultant at the University of Texas Medical Branch in Galveston, The University of Texas MD Anderson Cancer Center and the University of Texas School of Public Health. His main interest is in developing Bayesian methods for use in medical and biological problems and in authoring textbooks in statistics. His previous books for Chapman & Hall/CRC include Bayesian Biostatistics and Diagnostic Medicine, and Bayesian Methods for Agreement.
2019-04-16 By Lyle D. Broemeling

In Bayesian Biostatistics ( D. A. Berry and D. K. Stangl , eds ) , pp . 577-600 .
Marcel Dekker , New York . Allen - Mersh , T. G. , Earlam , S. , Fordy , C. , Abrams
, K. and Houghton , J. ( 1994 ) Quality - oflife and survival with continuous hepatic
 ...

Author: David J. Spiegelhalter

Publisher: John Wiley & Sons

ISBN: 0471499757

Category: Mathematics

Page: 408

View: 690

DOWNLOAD & READ
READ ALL ABOUT IT! David Spiegelhalter has recently joined the ranks of Isaac Newton, Charles Darwin and Stephen Hawking by becoming a fellow of the Royal Society. Originating from the Medical Research Council’s biostatistics unit, David has played a leading role in the Bristol heart surgery and Harold Shipman inquiries. Order a copy of this author’s comprehensive text TODAY! The Bayesian approach involves synthesising data and judgement in order to reach conclusions about unknown quantities and make predictions. Bayesian methods have become increasingly popular in recent years, notably in medical research, and although there are a number of books on Bayesian analysis, few cover clinical trials and biostatistical applications in any detail. Bayesian Approaches to Clinical Trials and Health-Care Evaluation provides a valuable overview of this rapidly evolving field, including basic Bayesian ideas, prior distributions, clinical trials, observational studies, evidence synthesis and cost-effectiveness analysis. Covers a broad array of essential topics, building from the basics to more advanced techniques. Illustrated throughout by detailed case studies and worked examples Includes exercises in all chapters Accessible to anyone with a basic knowledge of statistics Authors are at the forefront of research into Bayesian methods in medical research Accompanied by a Web site featuring data sets and worked examples using Excel and WinBUGS - the most widely used Bayesian modelling package Bayesian Approaches to Clinical Trials and Health-Care Evaluation is suitable for students and researchers in medical statistics, statisticians in the pharmaceutical industry, and anyone involved in conducting clinical trials and assessment of health-care technology.
2004-01-16 By David J. Spiegelhalter

0 - W32 . exe Thall , P . F . , Simon , R . and Estey , E . H . ( 1995 ) Bayesian
sequential monitoring designs for single - arm clinical trials with multiple ... In :
Bayesian Biostatistics ( D . Berry and D . Stangl , editors ) , Dekker , New York , 3
- 66 .

Author: American Statistical Association. Section on Bayesian Statistical Science

Publisher:

ISBN: UOM:39015054014496

Category: Bayesian statistical decision theory

Page:

View: 358

DOWNLOAD & READ

Osherson , D , Smith , E , Shafir , E , Gualtierotti , A and Biolsi , K ( 1995 ) A
source of Bayesian priors , Cognitive Science ... Raftery , A and Richardson , S (
1995 ) Model selection for generalized linear models , in Bayesian biostatistics ,
eds D ...

Author: P. Congdon

Publisher: John Wiley & Sons Incorporated

ISBN: STANFORD:36105110333601

Category: Mathematics

Page: 531

View: 380

DOWNLOAD & READ
Bayesian methods draw upon previous research findings and combine them with sample data to analyse problems and modify existing hypotheses. The calculations are often extremely complex, with many only now possible due to recent advances in computing technology. Bayesian methods have as a result gained wider acceptance, and are applied in many scientific disciplines, including applied statistics, public health research, medical science, the social sciences and economics. Bayesian Statistical Modelling presents an accessible overview of modelling applications from a Bayesian perspective. * Provides an integrated presentation of theory, examples and computer algorithms * Examines model fitting in practice using Bayesian principles * Features a comprehensive range of methodologies and modelling techniques * Covers recent innovations in bayesian modelling, including Markov Chain Monte Carlo methods * Includes extensive applications to health and social sciences * Features a comprehensive collection of nearly 200 worked examples * Data examples and computer code in WinBUGS are available via ftp Whilst providing a general overview of Bayesian modelling, the author places emphasis on the principles of prior selection, model identification and interpretation of findings, in a range of modelling innovations, focussing on their implementation with real data, with advice as to appropriate computing choices and strategies. Researchers in applied statistics, medical science, public health and the social sciences will benefit greatly from the examples and applications featured. The book will also appeal to graduate students of applied statistics, data analysis and Bayesian methods, and will provide a good reference source for both researchers and students.
2001-05-02 By P. Congdon

References Antoniak , C . E . , ( 1974 ) . Mixtures of Dirichlet Processes with
Applications to Bayesian Nonparametric Problems . Annals of Statistics , 2 , 1152
- 1174 . Berry , D . A . , and Stangl , D . K . ( 1996 ) . Bayesian Biostatistics , New
York ...

Author: Satyanshu K. Upadhyay

Publisher: Anshan Pub

ISBN: STANFORD:36105123399649

Category: Mathematics

Page: 507

View: 141

DOWNLOAD & READ
In the last two decades, Bayesian Statistics has acquired immense importance and has penetrated almost every area including those where the application of statistics appeared to be a remote possibility. This volume provides both theoretical and practical insights into the subject with detailed up-to-date material on various aspects. It serves two important objectives - to offer a thorough background material for theoreticians and gives a variety of applications for applied statisticians and practitioners. Consisting of 33 chapters, it covers topics on biostatistics, econometrics, reliability, image analysis, Bayesian computation, neural networks, prior elicitation, objective Bayesian methodologies, role of randomisation in Bayesian analysis, spatial data analysis, nonparametrics and a lot more. The book will serve as an excellent reference work for updating knowledge and for developing new methodologies in a wide variety of areas. It will become an invaluable tool for statisticians and the practitioners of Bayesian paradigm.

Bayesian modeling of binary repeated measures data with application to
crossover trials . In Bayesian Biostatistics , D. A. Berry and D. K. Stangl , eds . ,
New York : Marcel Dekker , pp . 577-599 . Bennett , J.E. , Racing - Poon , A. and
Wakefield ...

Author: Cheng Rong

Publisher:

ISBN: UCLA:L0082058173

Category:

Page: 278

View: 966

DOWNLOAD & READ
2000 By Cheng Rong

AN ESSENTIAL INTRODUCTORY TEXT LINKING TRADITIONAL
BIOSTATISTICS WITH BAYESIAN METHODS In recent years , Bayesian methods
have seen an explosion of interest , with applications in fields including
biochemistry , ecology ...

Author: George G. Woodworth

Publisher: Wiley-Interscience

ISBN: UCSC:32106017800191

Category: Mathematics

Page: 360

View: 563

DOWNLOAD & READ
An essential introductory text linking traditional biostatistics with bayesian methods In recent years, Bayesian methods have seen an explosion of interest, with applications in fields including biochemistry, ecology, medicine, oncology, pharmacology, and public health. As an interpretive system integrating data with observation, the Bayesian approach provides a nuanced yet mathematically rigorous means of conceptualizing biomedical statistics–from diagnostic tests to DNA evidence. Biostatistics: A Bayesian Introduction offers a pioneering approach by presenting the foundations of biostatistics through the Bayesian lens. Using easily understood, classic Dutch Book thought experiments to derive subjective probability from a simple principle of rationality, the book connects statistical science with scientific reasoning. The author shows how to compute, interpret, and report Bayesian statistical analyses in practice, and illustrates how to reinterpret traditional statistical reporting–such as confidence intervals, margins of error, and one-sided p-values–in Bayesian terms. Topics covered include: Probability and subjective probability Distributions and descriptive statistics Continuous probability distributions Comparing rates and means Linear models and statistical adjustment Logistic regression and adjusted odds ratios Survival analysis Hierarchical models and meta-analysis Decision theory and sample size determination The book includes extensive problem sets and references in each chapter, as well as complete instructions on computer analysis with the versatile SAS and WinBUGS software packages as well as the Excel spreadsheet program. For professionals and students, Biostatistics: A Bayesian Introduction offers an unique, real-world entry point into a remarkable alternative method of interpreting statistical data.
2004-09-06 By George G. Woodworth

Bayesian biostatistics . New York : Marcel Dekker , 141-156 . Chaloner KM and
Duncan GT ( 1983 ) . Assessment of a beta prior distribution : PM elicitation . The
Statistician , 32 , 174–180 . Chaloner K and Rhame FS ( 2001 ) . Quantifying and
 ...

Author: Ari Moshe Lipsky

Publisher:

ISBN: UCLA:L0099916058

Category:

Page: 424

View: 585

DOWNLOAD & READ

Expected Utility as a Policy Making Tool : An Environmental Health Example , ” in
Case Studies in Bayesian Biostatistics , D . Berry and D . Stangl , Ed . Marcel -
Dekker , New York , pp . 261 - 277 . Wolfson , L . J . , Kadane , J . B . , and Small ...

Author: Constantine Gatsonis

Publisher: Springer

ISBN: 0387949909

Category: Mathematics

Page: 471

View: 388

DOWNLOAD & READ
This third volume of case studies presents detailed applications of Bayesian statistical analysis, emphasising the scientific context. The papers were presented and discussed at a workshop held at Carnegie-Mellon University, and this volume - dedicated to the memory of Morrie Groot-reproduces six invited papers, each with accompanying invited discussion, and nine contributed papers with the focus on econometric applications.
1997-06-05 By Constantine Gatsonis

Bayesian approach (continued) treatment -covariate interactions 6:4560 to outlier
detection 4:2922 Bayesian calibration 1:463 Bayesian decision models, in health
care 1:262 Bayesian estimator 1:28; 2:1389; 5:3860 Bayesian inference 1:299; ...

Author: Peter Armitage

Publisher:

ISBN: 0471975761

Category: Mathematics

Page: 690

View: 276

DOWNLOAD & READ
1998 By Peter Armitage

As has been well discussed, the explosion of interest in Bayesian methods over
the last 10 to 20 years has been the ... The recent series of winter Bayesian
biostatistics conferences at the University of Texas M.D. Anderson Cancer Center
in ...

Author: Scott M. Berry

Publisher: CRC Press

ISBN: 1439825513

Category: Mathematics

Page: 323

View: 986

DOWNLOAD & READ
Already popular in the analysis of medical device trials, adaptive Bayesian designs are increasingly being used in drug development for a wide variety of diseases and conditions, from Alzheimer’s disease and multiple sclerosis to obesity, diabetes, hepatitis C, and HIV. Written by leading pioneers of Bayesian clinical trial designs, Bayesian Adaptive Methods for Clinical Trials explores the growing role of Bayesian thinking in the rapidly changing world of clinical trial analysis. The book first summarizes the current state of clinical trial design and analysis and introduces the main ideas and potential benefits of a Bayesian alternative. It then gives an overview of basic Bayesian methodological and computational tools needed for Bayesian clinical trials. With a focus on Bayesian designs that achieve good power and Type I error, the next chapters present Bayesian tools useful in early (Phase I) and middle (Phase II) clinical trials as well as two recent Bayesian adaptive Phase II studies: the BATTLE and ISPY-2 trials. In the following chapter on late (Phase III) studies, the authors emphasize modern adaptive methods and seamless Phase II–III trials for maximizing information usage and minimizing trial duration. They also describe a case study of a recently approved medical device to treat atrial fibrillation. The concluding chapter covers key special topics, such as the proper use of historical data, equivalence studies, and subgroup analysis. For readers involved in clinical trials research, this book significantly updates and expands their statistical toolkits. The authors provide many detailed examples drawing on real data sets. The R and WinBUGS codes used throughout are available on supporting websites. Scott Berry talks about the book on the CRC Press YouTube Channel.
2010-07-19 By Scott M. Berry

His previous books are Bayesian Analysis of Linear Models, Econometrics and
Structural Change, written with Hiroki Tsurumi, and Bayesian Biostatistics and
Diagnostic Medicine. 1 Introduction to Agreement 1.1 Introduction This book is an
xv ...

Author: Lyle D. Broemeling

Publisher: CRC Press

ISBN: 1420083430

Category: Mathematics

Page: 340

View: 939

DOWNLOAD & READ
Using WinBUGS to implement Bayesian inferences of estimation and testing hypotheses, Bayesian Methods for Measures of Agreement presents useful methods for the design and analysis of agreement studies. It focuses on agreement among the various players in the diagnostic process. The author employs a Bayesian approach to provide statistical inferences based on various models of intra- and interrater agreement. He presents many examples that illustrate the Bayesian mode of reasoning and explains elements of a Bayesian application, including prior information, experimental information, the likelihood function, posterior distribution, and predictive distribution. The appendices provide the necessary theoretical foundation to understand Bayesian methods as well as introduce the fundamentals of programming and executing the WinBUGS software. Taking a Bayesian approach to inference, this hands-on book explores numerous measures of agreement, including the Kappa coefficient, the G coefficient, and intraclass correlation. With examples throughout and end-of-chapter exercises, it discusses how to successfully design and analyze an agreement study.
2009-01-12 By Lyle D. Broemeling

Spiegelhalter DJ , Myles P , Jones DR , Abrams KR . An introduction to bayesian
methods in health technology assessment . BMJ 1999 ; 319 : 508-12 . 2. Berry
D A , Stangl D K. Bayesian biostatistics . New York : Marcel Dekker Inc , 1996 . 3.

Author: Academy of Medicine (Singapore)

Publisher:

ISBN: UCLA:L0088703616

Category:

Page:

View: 641

DOWNLOAD & READ

Best Books