{"product_id":"network-meta-analysis-for-decision-making-hardcover","title":"Network Meta-Analysis for Decision-Making - Hardcover","description":"\u003cdiv\u003e\u003cp style=\"text-align: right;\"\u003e\u003ca href=\"https:\/\/reportcopyrightinfringement.com\/\" target=\"_blank\" rel=\"nofollow\"\u003e\u003cb\u003eReport copyright infringement\u003c\/b\u003e\u003c\/a\u003e\u003c\/p\u003e\u003c\/div\u003e\u003cp\u003eby \u003cb\u003eSofia Dias\u003c\/b\u003e (Author), \u003cb\u003eA. E. Ades\u003c\/b\u003e (Author), \u003cb\u003eNicky J. Welton\u003c\/b\u003e (Author)\u003c\/p\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003eA practical guide to network meta-analysis with examples and code\u003c\/b\u003e\u003c\/p\u003e \u003cp\u003eIn 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 \u003cb\u003e\u003ci\u003etwo or more\u003c\/i\u003e\u003c\/b\u003e 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'?\"\u003c\/p\u003e \u003cp\u003eA 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.\u003c\/p\u003e \u003cp\u003eThis 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.\u003c\/p\u003e \u003cul\u003e \u003cli\u003eMethods used throughout this book can be applied consistently: model critique and checking for evidence consistency are emphasised.\u003c\/li\u003e \u003cli\u003eMethods are based on technical support documents produced for NICE Decision Support Unit, which support the NICE Methods of Technology Appraisal.\u003c\/li\u003e \u003cli\u003eCode presented is also the basis for the code used by the ISPOR Task Force on Indirect Comparisons.\u003c\/li\u003e \u003cli\u003eIncludes extensive carefully worked examples, with thorough explanations of how to set out data for use in WinBUGS and how to interpret the output.\u003c\/li\u003e \u003c\/ul\u003e \u003cp\u003e\u003ci\u003eN\u003c\/i\u003e\u003ci\u003eetwork Meta-Analysis for Decision Making \u003c\/i\u003ewill be of interest to decision makers, medical statisticians, health economists, and anyone involved in Health Technology Assessment including the pharmaceutical industry.\u003c\/p\u003e\u003ch3\u003eFront Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003e A practical guide to network meta-analysis with examples and code \u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn 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. \u003ci\u003eNetwork Meta-Analysis for Decision-Making\u003c\/i\u003e takes an approach to evidence synthesis that is specifically intended for decision making when there are \u003cem\u003e\u003cstrong\u003etwo or more\u003c\/strong\u003e\u003c\/em\u003e 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'? \u003c\/p\u003e\u003cp\u003e 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. \u003c\/p\u003e\u003cp\u003e 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eMethods used throughout this book can be applied consistently: model critique and checking for evidence consistency are emphasised\u003c\/li\u003e \u003cli\u003eMethods are based on technical support documents produced for NICE Decision Support Unit, which support the NICE Methods of Technology Appraisal\u003c\/li\u003e \u003cli\u003eCode presented is also the basis for the code used by the ISPOR Task Force on Indirect Comparisons\u003c\/li\u003e \u003cli\u003eIncludes extensive carefully worked examples, with thorough explanations of how to set out data for use in WinBUGS and how to interpret the output\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e \u003cp\u003e\u003ci\u003e Network Meta-Analysis for Decision-Making\u003c\/i\u003e will be of interest to decision makers, medical statisticians, health economists, and anyone involved in Health Technology Assessment including the pharmaceutical industry.\u003c\/p\u003e\u003ch3\u003eBack Jacket\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003e A practical guide to network meta-analysis with examples and code \u003c\/b\u003e \u003c\/p\u003e\u003cp\u003eIn 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. \u003ci\u003eNetwork Meta-Analysis for Decision-Making\u003c\/i\u003e takes an approach to evidence synthesis that is specifically intended for decision making when there are \u003cem\u003e\u003cstrong\u003etwo or more\u003c\/strong\u003e\u003c\/em\u003e 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'?\" \u003c\/p\u003e\u003cp\u003e 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. \u003c\/p\u003e\u003cp\u003e 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. \u003c\/p\u003e\u003cul\u003e \u003cli\u003eMethods used throughout this book can be applied consistently: model critique and checking for evidence consistency are emphasised\u003c\/li\u003e \u003cli\u003eMethods are based on technical support documents produced for NICE Decision Support Unit, which support the NICE Methods of Technology Appraisal\u003c\/li\u003e \u003cli\u003eCode presented is also the basis for the code used by the ISPOR Task Force on Indirect Comparisons\u003c\/li\u003e \u003cli\u003eIncludes extensive carefully worked examples, with thorough explanations of how to set out data for use in WinBUGS and how to interpret the output\u003c\/li\u003e \u003c\/ul\u003e \u003cbr\u003e \u003cp\u003e\u003ci\u003e Network Meta-Analysis for Decision-Making\u003c\/i\u003e will be of interest to decision makers, medical statisticians, health economists, and anyone involved in Health Technology Assessment including the pharmaceutical industry.\u003c\/p\u003e\u003ch3\u003eAuthor Biography\u003c\/h3\u003e\u003cp\u003e\u003c\/p\u003e\u003cp\u003e\u003cb\u003e SOFIA DIAS, \u003c\/b\u003e\u003ci\u003e University of Bristol, UK\u003c\/i\u003e\u003cbr\u003e \u003cb\u003eA.E. ADES, \u003c\/b\u003e\u003ci\u003e University of Bristol, UK\u003c\/i\u003e\u003cbr\u003e \u003cb\u003eNICKY J. WELTON, \u003c\/b\u003e\u003ci\u003e University of Bristol, UK\u003c\/i\u003e\u003cbr\u003e \u003cb\u003eJEROEN P. JANSEN, \u003c\/b\u003e\u003ci\u003e Precision Health Economics, USA\u003c\/i\u003e\u003cbr\u003e \u003cb\u003eALEXANDER J. SUTTON, \u003c\/b\u003e\u003ci\u003e University of Leicester, UK\u003c\/i\u003e\u003cbr\u003e\u003c\/p\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eNumber of Pages:\u003c\/strong\u003e 488\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003eDimensions:\u003c\/strong\u003e 1.1 x 9.1 x 6 IN\u003c\/div\u003e\n            \u003cdiv\u003e\n\u003cstrong\u003ePublication Date:\u003c\/strong\u003e March 19, 2018\u003c\/div\u003e\n            ","brand":"BooksCloud","offers":[{"title":"Default Title","offer_id":47336707522809,"sku":"9781118647509","price":142.49,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0789\/2782\/3097\/files\/ZDNLSkZndTVZNFZoVGtDbmVtQVVmUT09.webp?v=1769670857","url":"https:\/\/bookscloud.io\/products\/network-meta-analysis-for-decision-making-hardcover","provider":"BooksCloud Book Dropshipping","version":"1.0","type":"link"}