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.
Autorentext
Sir David John Spiegelhalter, OBE FRS, is a British statistician and Winton Professor of the Public Understanding of Risk in the Statistical Laboratory at the University of Cambridge and a Fellow of Churchill College, Cambridge. Spiegelhalter is an ISI highly cited researcher.
Keith R. Abrams is the author of Bayesian Approaches to Clinical Trials and Health-Care Evaluation, published by Wiley.
Jonathan P. Myles is the author of Bayesian Approaches to Clinical Trials and Health-Care Evaluation, published by Wiley.
Klappentext
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.
Zusammenfassung
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.
Inhalt
Preface.
List of examples.
1. Introduction.
1.1 What are Bayesian methods?
1.2 What do we mean by 'health-care evaluation'?
1.3 A Bayesian approach to evaluation.
1.4 The aim of this book and the intended audience.
1.5 Structure of the book.
2. Basic Concepts from Traditional Statistical Analysis.
2.1 Probability.
2.1.1 What is probability?
2.1.2 Odds and log-odds.
2.1.3 Bayes theorem for simple events.
2.2 Random variables, parameters and likelihood.
2.2.1 Random variables and their distributions.
2.2.2 Expectation, variance, covariance and correlation.
2.2.3 Parametric distributions and conditional independence.
2.2.4 Likelihoods.
2.3 The normal distribution.
2.4 Normal likelihoods.
2.4.1 Normal approximations for binary data.
2.4.2 Normal likelihoods for survival data.
2.4.3 Normal likelihoods for count responses.
2.4.4 Normal likelihoods for continuous responses.
2.5 Classical inference.
2.6 A catalogue of useful distributions*.
2.6.1 Binomial and Bernoulli.
2.6.2 Poisson.
2.6.3 Beta.
2.6.4 Uniform.
2.6.5 Gamma.
2.6.6 Root-inverse-gamma.
2.6.7 Half-normal.
2.6.8 Log-normal.
2.6.9 Student's t.
2.6.10 Bivariate normal.
2.7 Key points.
Exercises.
3. An Overview of the Bayesian Approach.
3.1 Subjectivity and context.
3.2 Bayes theorem for two hypotheses.
3.3 Comparing simple hypotheses: likelihood ratios and Bayes factors.
3.4 Exchangeability and parametric modelling*.
3.5 Bayes theorem for general quantities.
3.6 Bayesian analysis with binary data.
3.6.1 Binary data with a discrete prior distribution.
3.6.2 Conjugate analysis for binary data.
3.7 B…