Данная книга является практическим руководством по сетевому мета-анализу с примерами и кодом. В оценке здравоохранения необходимы строгие методы количественной оценки, чтобы определить, какие вмешательства являются эффективными и экономически эффективными. Часто одного исследования недостаточно, и желательно обобщить данные из нескольких источников, обычно рандомизированных контролируемых испытаний. Автор книги предлагает подход к
В книге рассматриваются практические аспекты использования сетевого мета - анализа для принятия решений в медицинской сфере и отвечает на вопрос "Какое лечение лучше для этого предварительно определенного когорты пациентов? "
In this authoritative guide, Sofia Dias describes the type of evidence synthesis needed for ranking various treatments based on their effectiveness in a population of patients. The methods presented fit into a comprehensive framework and are taught with classic examples and practical exercises that require the reader to adapt the material to their cases. Various advanced techniques are also covered, providing a window into some of the more intricate aspects of this type of analysis.
This manual fits the bill: it does not assume the reader possess any prior knowledge of statistics or Bayesian methods, but takes you through them step by step while presenting the results visually and graphically. Everything is presented in an intuitive manner with clearly written summaries that keep even the most seasoned reader engaged.
You will learn about the inner workings of mixed treatment comparisons and Bayesian Markov chain methods with ease. From chapter to chapter, you methodically develop your own working file with simulated datasets before conducting your meta analysis, allowing you to walk hand in hand with the author step by step. Exercises designed throughout the book test your knowledge and transfer it systematically into fully fledged meta analyses that you save as Exel files that you can re-run if you come across interesting new configurations or scenarios.
A great addition to any statistics book club.
Электронная Книга «Network Meta-Analysis for Decision-Making» написана автором Sofia Dias в году.
Минимальный возраст читателя: 0
Язык: Английский
ISBN: 9781118951712
Описание книги от Sofia Dias
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. Methods used throughout this book can be applied consistently: model critique and checking for evidence consistency are emphasised. Methods are based on technical support documents produced for NICE Decision Support Unit, which support the NICE Methods of Technology Appraisal. Code presented is also the basis for the code used by the ISPOR Task Force on Indirect Comparisons. Includes extensive carefully worked examples, with thorough explanations of how to set out data for use in WinBUGS and how to interpret the output. 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.
