In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are both effective and cost-effective. Usually a single study will not fully address these issues and it is desirable to synthesize evidence from multiple sources. This book aims to provide a practical guide to evidence synthesis for the purpose of decision making, starting with a simple single parameter model, where all studies estimate the same quantity (pairwise meta-analysis) and progressing to more complex multi-parameter structures (including meta-regression, mixed treatment comparisons, Markov models of disease progression, and epidemiology models). A comprehensive, coherent framework is adopted and estimated using Bayesian methods. 
Key features: <ul>
<li>A coherent approach to evidence synthesis from multiple sources.</li> <li>Focus is given to Bayesian methods for evidence synthesis that can be integrated within cost-effectiveness analyses in a probabilistic framework using Markov Chain Monte Carlo simulation.</li> <li>Provides methods to statistically combine evidence from a range of evidence structures.</li> <li>Emphasizes the importance of model critique and checking for evidence consistency.</li> <li>Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book.</li> <li>WinBUGS code is provided for all examples.</li> </ul>
Evidence Synthesis for Decision Making in Healthcare is intended for health economists, decision modelers, statisticians and others involved in evidence synthesis, health technology assessment, and economic evaluation of health technologies.

Evidence Synthesis for Decision Making in Healthcare

Science & Math

Cooper, Nicola

Ades, A. E.

Welton, Nicky J.

Sutton, Alexander J.

Abrams, Keith R.

Biological Sciences

wiley

With meta-analysis methods playing a crucial role in health research in recent years, this important and clearly-written book provides a much-needed survey of the field. Meta-analysis provides a framework for combining the results of several clinical trials and drawing inferences about the effectiveness of medical treatments. The move towards evidence-based health care and practice is underpinned by the use of meta-analysis. This book: * Provides a thorough criticism and an up-to-date survey of meta-analysis methods * Emphasises the practical approach, and illustrates the methods by numerous examples * Describes the use of Bayesian methods in meta-analysis * Includes discussion of appropriate software for each analysis * Includes numerous references to more advanced treatment of specialist topics * Refers to software code used in the examples available on the authors' Web site Practising statisticians, statistically-minded clinicians and health research professionals will benefit greatly from the clear presentation and numerous examples. Medical researchers will grasp the basic principles of meta-analysis, and learn how to apply the various methods.

Methods for Meta-Analysis in Medical Research

Jones, David R.

Sheldon, Trevor A.

Song, Fujian

A practical guide to network meta-analysis with examples and code In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish which interventions are effective and cost-effective. Often a single study will not provide the answers and it is desirable to synthesise evidence from multiple sources, usually randomised controlled trials. This book takes an approach to evidence synthesis that is specifically intended for decision making when there are two or more treatment alternatives being evaluated, and assumes that the purpose of every synthesis is to answer the question "for this pre-identified population of patients, which treatment is 'best'?" A comprehensive, coherent framework for network meta-analysis (mixed treatment comparisons) is adopted and estimated using Bayesian Markov Chain Monte Carlo methods implemented in the freely available software WinBUGS. Each chapter contains worked examples, exercises, solutions and code that may be adapted by readers to apply to their own analyses. This book can be used as an introduction to evidence synthesis and network meta-analysis, its key properties and policy implications. Examples and advanced methods are also presented for the more experienced reader. <ul>
<li>Methods used throughout this book can be applied consistently: model critique and checking for evidence consistency are emphasised.</li> <li>Methods are based on technical support documents produced for NICE Decision Support Unit, which support the NICE Methods of Technology Appraisal.</li> <li>Code presented is also the basis for the code used by the ISPOR Task Force on Indirect Comparisons.</li> <li>Includes extensive carefully worked examples, with thorough explanations of how to set out data for use in WinBUGS and how to interpret the output.</li> </ul>
Network Meta-Analysis for Decision Making will be of interest to decision makers, medical statisticians, health economists, and anyone involved in Health Technology Assessment including the pharmaceutical industry.

Network Meta-Analysis for Decision-Making (Statistics in Practice)

Dias, Sofia

Jansen, Jeroen P.

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