Tools for Collaborative Decision-Making

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.

Medical Decision Making provides clinicians with a powerful framework for helping patients make decisions that increase the likelihood that they will have the outcomes that are most consistent with their preferences. This new edition provides a thorough understanding of the key decision making infrastructure of clinical practice and explains the principles of medical decision making both for individual patients and the wider health care arena. It shows how to make the best clinical decisions based on the available evidence and how to use clinical guidelines and decision support systems in electronic medical records to shape practice guidelines and policies. Medical Decision Making is a valuable resource for all experienced and learning clinicians who wish to fully understand and apply decision modelling, enhance their practice and improve patient outcomes. “There is little doubt that in the future many clinical analyses will be based on the methods described in Medical Decision Making, and the book provides a basis for a critical appraisal of such policies.” – Jerome P. Kassirer M.D., Distinguished Professor, Tufts University School of Medicine, US and Visiting Professor, Stanford Medical School, US

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.

This book outlines the creative process of making environmental management decisions using the approach called Structured Decision Making. It is a short introductory guide to this popular form of decision making and is aimed at environmental managers and scientists. This is a distinctly pragmatic label given to ways for helping individuals and groups think through tough multidimensional choices characterized by uncertain science, diverse stakeholders, and difficult tradeoffs. This is the everyday reality of environmental management, yet many important decisions currently are made on an ad hoc basis that lacks a solid value-based foundation, ignores key information, and results in selection of an inferior alternative. Making progress – in a way that is rigorous, inclusive, defensible and transparent – requires combining analytical methods drawn from the decision sciences and applied ecology with deliberative insights from cognitive psychology, facilitation and negotiation. The authors review key methods and discuss case-study examples based in their experiences in communities, boardrooms, and stakeholder meetings. The goal of this book is to lay out a compelling guide that will change how you think about making environmental decisions. Visit www.wiley.com/go/gregory/sdm to access the figures and tables from the book.

Vital Notes for Nurses: Professional Development, Reflection and Decision-making provides a concise, accessible introduction to professional development, reflective practice and clinical decision-making. Vital Notes for Nurses: Professional Development, Reflection and Decision-making explores the core strategies of reflective practice and decision-making underlying professional nursing development. Separate chapters on reflective writing, evidence-based practice, clinical supervision and portfolios demonstrate the inter-relationship between professional development and professional practice. It is aimed at both student and qualified nurses, recognising that professional development is a life-long commitment. It provides clear guidance to help practitioners think critically about their actions, work within professional boundaries, be accountable for their actions and plan for their future. * Provides a concise introduction to professional development, reflective practice, and clinical decision-making * Written in a clear accessible style which assumes no prior knowledge * Enables students to consider and develop their practice in order to become competent practitioners * Each chapter includes learning objectives, scenarios and case studies * In the Vital Notes for Nurses series

Data mining is the process of automatically searching large volumes of data for models and patterns using computational techniques from statistics, machine learning and information theory; it is the ideal tool for such an extraction of knowledge. Data mining is usually associated with a business or an organization's need to identify trends and profiles, allowing, for example, retailers to discover patterns on which to base marketing objectives. This book looks at both classical and recent techniques of data mining, such as clustering, discriminant analysis, logistic regression, generalized linear models, regularized regression, PLS regression, decision trees, neural networks, support vector machines, Vapnik theory, naive Bayesian classifier, ensemble learning and detection of association rules. They are discussed along with illustrative examples throughout the book to explain the theory of these methods, as well as their strengths and limitations. Key Features: Presents a comprehensive introduction to all techniques used in data mining and statistical learning, from classical to latest techniques. Starts from basic principles up to advanced concepts. Includes many step-by-step examples with the main software (R, SAS, IBM SPSS) as well as a thorough discussion and comparison of those software. Gives practical tips for data mining implementation to solve real world problems. Looks at a range of tools and applications, such as association rules, web mining and text mining, with a special focus on credit scoring. Supported by an accompanying website hosting datasets and user analysis. Statisticians and business intelligence analysts, students as well as computer science, biology, marketing and financial risk professionals in both commercial and government organizations across all business and industry sectors will benefit from this book.

An increasing number of business executives, managers and political leaders are using the concept of “weak signals” nowadays. There are also an increasing number of people trying to find out exactly what this concept means, as well as when and how to use it. Such questions arise particularly when it comes down to making strategic decisions. To help with these questions, this book defines the concept of the “weak signal” and then demonstrates how it would be useful in relation to strategic decisions, and more precisely in relation to three moments of the strategic decision, i.e.: – The trigger of the strategic decision (what is the trigger, why and when) – The development of the strategic decision – The implementation of the strategic decision

Science reassures, art disturbs (Proverb) This intriguing exploration of underlying forces in decision making takes as its starting point a wealth of high profile decision disasters. In brilliantly readable analyses, Helga Drummond shows how better awareness of the inherent uncertainties of the decision making process could have made the outcomes very different. Examples showcased include: The Hatfield rail crash The Kursk submarine disaster The Challenger disaster The year 2000 fuel crisis The WWII Dardanelles expedition The Barings Bank collapse The Taurus Stock Exchange Project The Hillsborough tragedy The King's Cross underground fire The Millennium Dome This entertaining yet instructive book offers new insight into the realities of decision making, and shows how you can confront them to improve your prospects of success.

The only book to combine emergency management principLEs with proven military concepts Good disaster plans do not guarantee a good response. Any disaster plan rarely survives the first rain bands of a hurricane or the first tremors of an earthquake. While developing plans is essential, there must be systems in place to adapt these plans to the ever-changing operational environment of a disaster. Currently there is no set of standard disaster response principles to guide a community. The National Incident Management System (NIMS) and the Incident Command System (ICS) provide the framework to implement operational decisions, but they were never designed as operational concepts. The military has developed just such concepts and many of them can be adapted for civilian use. Disaster Operations and Decision Making adapts those military concepts and combines them with disaster lessons learned to create a new opera-tional paradigm. Emphasizing team building, Emergency Operations Center operational systems, and situational awareness, the book details easily adopted methods. All of these methods are designed to be incorporated into the NIMS and ICS framework to enhance a community's response to any type of disaster. Disaster Operations and Decision Making is an essential resource for emergency managers, fire chiefs, law enforcement officers, homeland security professionals, public health officials, and anyone else involved or interested in crisis management.

This book is intended for use by natural resource managers and scientists, and students in the fields of natural resource management, ecology, and conservation biology, who are confronted with complex and difficult decision making problems. The book takes readers through the process of developing a structured approach to decision making, by firstly deconstructing decisions into component parts, which are each fully analyzed and then reassembled to form a working decision model. The book integrates common-sense ideas about problem definitions, such as the need for decisions to be driven by explicit objectives, with sophisticated approaches for modeling decision influence and incorporating feedback from monitoring programs into decision making via adaptive management. Numerous worked examples are provided for illustration, along with detailed case studies illustrating the authors’ experience in applying structured approaches. There is also a series of detailed technical appendices. An accompanying website provides computer code and data used in the worked examples. Additional resources for this book can be found at: www.wiley.com/go/conroy/naturalresourcemanagement.