9780470061091 - Evidence Synthesis for Decision Making in Healthcare: 132 (Statistics in Practice) de Welton, Nicky J.; Sutton, Alexander J.; Cooper, Nicola; Abrams, Keith R.; Ades... (20 resultados)

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Hardback. Condición: New. 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: A coherent approach to evidence synthesis from multiple sources.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.Provides methods to statistically combine evidence from a range of evidence structures.Emphasizes the importance of model critique and checking for evidence consistency.Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book.WinBUGS code is provided for all examples. 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.

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Hardcover, boards in decent condition, minimal signs of wear. Internally, all pages appear clean and bright, apart from owners name scribbled out in pen on rear of front board, no other indications of markings or annotation. Seller image provided. For further helpful synopsis and reviews try clicking on 'bookseller image'. Selling books since 1999, all proceeds help fund LOROS Charity.

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Condición: New. 2012. 1st Edition. Hardcover. In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are beneficial, are superior to all alternatives and are cost-effective. Usually one study will not provide answers to these questions and it will be necessary to synthesize evidence from multiple sources. Series: Statistics in Practice. Num Pages: 294 pages, Illustrations. BIC Classification: MB; MJ; PBT. Category: (P) Professional & Vocational. Dimension: 235 x 157 x 19. Weight in Grams: 510. . . . . .

Condición: New. pp. 294.

Condición: New. 2012. 1st Edition. Hardcover. In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are beneficial, are superior to all alternatives and are cost-effective. Usually one study will not provide answers to these questions and it will be necessary to synthesize evidence from multiple sources. Series: Statistics in Practice. Num Pages: 294 pages, Illustrations. BIC Classification: MB; MJ; PBT. Category: (P) Professional & Vocational. Dimension: 235 x 157 x 19. Weight in Grams: 510. . . . . . Books ship from the US and Ireland.

Gebunden. Condición: New. In the evaluation of healthcare, rigorous methods of quantitative assessment arenecessary 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 evi.

Hardback. Condición: New. 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: A coherent approach to evidence synthesis from multiple sources.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.Provides methods to statistically combine evidence from a range of evidence structures.Emphasizes the importance of model critique and checking for evidence consistency.Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book.WinBUGS code is provided for all examples. 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.

Buch. Condición: Neu. Neuware - 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:\* A coherent approach to evidence synthesis from multiple sources.\* 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.\* Provides methods to statistically combine evidence from a range of evidence structures.\* Emphasizes the importance of model critique and checking for evidence consistency.\* Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book.\* WinBUGS code is provided for all examples.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.

Hardcover. Condición: new. Hardcover. 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: A coherent approach to evidence synthesis from multiple sources.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.Provides methods to statistically combine evidence from a range of evidence structures.Emphasizes the importance of model critique and checking for evidence consistency.Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book.WinBUGS code is provided for all examples. 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. In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are beneficial, are superior to all alternatives and are cost-effective. Usually one study will not provide answers to these questions and it will be necessary to synthesize evidence from multiple sources. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.

Hardcover. Condición: Brand New. 1st edition. 294 pages. 9.13x6.24x0.75 inches. In Stock. This item is printed on demand.

Hardcover. Condición: new. Hardcover. 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: A coherent approach to evidence synthesis from multiple sources.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.Provides methods to statistically combine evidence from a range of evidence structures.Emphasizes the importance of model critique and checking for evidence consistency.Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book.WinBUGS code is provided for all examples. 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. In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are beneficial, are superior to all alternatives and are cost-effective. Usually one study will not provide answers to these questions and it will be necessary to synthesize evidence from multiple sources. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.

Hardcover. Condición: new. Hardcover. 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: A coherent approach to evidence synthesis from multiple sources.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.Provides methods to statistically combine evidence from a range of evidence structures.Emphasizes the importance of model critique and checking for evidence consistency.Presents numerous worked examples, exercises and solutions drawn from a variety of medical disciplines throughout the book.WinBUGS code is provided for all examples. 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. In the evaluation of healthcare, rigorous methods of quantitative assessment are necessary to establish interventions that are beneficial, are superior to all alternatives and are cost-effective. Usually one study will not provide answers to these questions and it will be necessary to synthesize evidence from multiple sources. This item is printed on demand. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.