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.

Decision making is a critical element in the field of medicine that can lead to life-or-death outcomes, yet it is an element fraught with complex and conflicting variables, diagnostic and therapeutic uncertainties, patient preferences and values, and costs. Together, decisions made by physicians, patients, insurers, and policymakers determine the quality of health care, quality that depends inherently on counterbalancing risks and benefits and competing objectives such as maximizing life expectancy versus optimizing quality of life or quality of care versus economic realities. Broadly speaking, concepts in medical decision making (MDM) may be divided into two major categories: prescriptive and descriptive. Work in the area of prescriptive MDM investigates how medical decisions should be done using complicated analyses and algorithms to determine cost-effectiveness measures, prediction methods, and so on. In contrast, descriptive MDM studies how decisions actually are made involving human judgment, biases, social influences, patient factors, and so on. The Encyclopedia of Medical Decision Making gives a gentle introduction to both categories, revealing how medical and healthcare decisions are actually made—and constrained—and how physician, healthcare management, and patient decision making can be improved to optimize health outcomes. Key Features Discusses very general issues that span many aspects of MDM, including bioethics; health policy and economics; disaster simulation modeling; medical informatics; the psychology of decision making; shared and team medical decision making; social, moral, and religious factors; end-of-life decision making; assessing patient preference and patient adherence; and more Incorporates both quantity and quality of life in optimizing a medical decision Considers characteristics of the decisionmaker and how those characteristics influence their decisions Presents outcome measures to judge the quality or impact of a medical decision Examines some of the more commonly encountered biostatistical methods used in prescriptive decision making Provides utility assessment techniques that facilitate quantitative medical decision making Addresses the many different assumption perspectives the decision maker might choose from when trying to optimize a decision Offers mechanisms for defining MDM algorithms With comprehensive and authoritative coverage by experts in the fields of medicine, decision science and cognitive psychology, and healthcare management, this two-volume Encyclopedia is a must-have resource for any academic library.

Evidence Synthesis in Healthcare – a Practical Handbook for Clinicians is the first book to reveal the field of Evidence Synthesis, by combining multiple sources of quantitative/qualitative data to derive the best evidence for use in healthcare. Through the use of clearly explained examples and practical explanations, Evidence Synthesis in Healthcare – a Practical Handbook for Clinicians describes the practical tools, techniques, uses and policy considerations of evidence synthesis techniques in modern healthcare practice.

The Encyclopedic Reference of Public Health presents the most important definitions, principles and general perspectives of public health, written by experts of the different fields. The work includes more than 2,500 alphabetical entries. Entries comprise review-style articles, detailed essays and short definitions. Numerous figures and tables enhance understanding of this little-understood topic. Solidly structured and inclusive, this two-volume reference is an invaluable tool for clinical scientists and practitioners in academia, health care and industry, as well as students, teachers and interested laypersons.

Six volumes combine to make up the Handbook of Complementary, Alternative, and Integrative Medicine: Education, Practice, and Research, which describes the education, practice, and research-related issues of complementary, alternative, and integrative medicine; and its efficacy and safety in treating diseases and health conditions. Further the volumes review integrative medicine around the world to share the best practices and experiences in terms of education, practice, and research; and identify the challenges and suggest recommendations to overcome them. The 20 chapters of Volume 3 focus on complementary, alternative, and integrative medicine research-related issues. It begins with a history of complementary, alternative, and integrative medicine research and continues with coverage of the different research issues such as complementary and alternative medicine (CAM) research importance, plans, proposals, funding, ethical issues, study designs, methods, advantages and disadvantages for each method, best practices in CAM research, and publications. Key Features: · Describes the complementary, alternative, and integrative medicine research-related issues · Describes the complementary and alternative medicine importance, plans and funding issues · Describes the complementary, alternative, and integrative medicine research methodology with examples · Describes the challenges of complementary, alternative, and integrative medicine research and suggest recommendations to overcome them

A revolution in American medicine is in full swing, with the race from fee-for-service to fee-for-value at the front line in an epic battle that will transform healthcare delivery for decades to come. In America’s Healthcare Transformation, eminent physician leader Robert A. Phillips brings together key thought leaders and trail-blazing practitioners, who provide a wide-ranging exploration of the strategies, innovations, and paradigm shifts that are driving this healthcare transformation. The contributors offer a panoramic look at the dramatic changes happening in the field of medicine, changes that put the patient at the heart of the process. Among other subjects, the essays evaluate innovative high quality and low cost care delivery solutions from around the United States and abroad, describe fundamental approaches to measuring the safety of care and the impact that guidelines have on improving quality of care and outcomes, and make a strong case that insurance reform will fundamentally and irreversibly drive delivery reform. In addition, America’s Healthcare Transformation reviews the role of health information technology in creating safer healthcare, provides a primer on the development of a culture of safety, and highlights ground-breaking new ways to train providers in patient safety and quality. Finally, the book looks at reports from Stanford Health Care and Houston Methodist which outline how successful behaviorally based strategies, anchored in values, can energize and empower employees to deliver a superior patient experience. Drawing on the wisdom and vision of today’s leading healthcare innovators, America’s Healthcare Transformation provides a roadmap to the future of American healthcare. This book is essential reading for all health care providers, health care administrators, and health policy professionals, and it will be an invaluable resource in the effort to improve the practice of medicine and the delivery of healthcare in our communities and nation.

An Introduction to Metascience delves into core metascientific concepts, offering a critical examination of current knowledge creation processes and scrutinising researchers and their methodologies across disciplines. This book stands alone as a comprehensive guide to metascience, offering readers a singular resource for understanding and implementing metascientific principles into their research practices. Readers will find this book invaluable for perfecting their research skills and enhancing the quality of their academic work. It exposes the reader to the intricacies of research processes, prompting a reevaluation of preconceived notions and fostering a deeper understanding of the flaws and solutions inherent in knowledge creation. Furthermore, it offers thought-provoking insights into implementing strategies to enhance research productivity, and it elucidates both the benefits and pitfalls of incorporating artificial intelligence in research production. Designed for scientists and researchers seeking to gain insight into the scientific process, An Introduction to Metascience caters to those interested in understanding how research evolves over time. It appeals to individuals eager to explore methods, practices, and philosophies of science to refine their approach to knowledge creation.

This book provides the reader with a comprehensive set of instructions and examples of how to perform an economic evaluation of a health intervention, focusing solely on cost-effectiveness analysis in healthcare.

Statistical Methods in Healthcare In recent years the number of innovative medicinal products and devices submitted and approved by regulatory bodies has declined dramatically. The medical product development process is no longer able to keep pace with increasing technologies, science and innovations and the goal is to develop new scientific and technical tools and to make product development processes more efficient and effective. Statistical Methods in Healthcare focuses on the application of statistical methodologies to evaluate promising alternatives and to optimize the performance and demonstrate the effectiveness of those that warrant pursuit is critical to success. Statistical methods used in planning, delivering and monitoring health care, as well as selected statistical aspects of the development and/or production of pharmaceuticals and medical devices are also addressed. With a focus on finding solutions to these challenges, this book: Provides a comprehensive, in-depth treatment of statistical methods in healthcare, along with a reference source for practitioners and specialists in health care and drug development. Offers a broad coverage of standards and established methods through leading edge techniques. Uses an integrated case study based approach, with focus on applications. Looks at the use of analytical and monitoring schemes to evaluate therapeutic performance. Features the application of modern quality management systems to clinical practice, and to pharmaceutical development and production processes. Addresses the use of modern statistical methods such as Adaptive Design, Seamless Design, Data Mining, Bayesian networks and Bootstrapping that can be applied to support the challenging new vision. Practitioners in healthcare-related professions, ranging from clinical trials to care delivery to medical device design, as well as statistical researchers in the field, will benefit from this book.

This BASS book Series publishes selected high-quality papers reflecting recent advances in the design and biostatistical analysis of biopharmaceutical experiments – particularly biopharmaceutical clinical trials. The papers were selected from invited presentations at the Biopharmaceutical Applied Statistics Symposium (BASS), which was founded by the first Editor in 1994 and has since become the premier international conference in biopharmaceutical statistics. The primary aims of the BASS are: 1) to raise funding to support graduate students in biostatistics programs, and 2) to provide an opportunity for professionals engaged in pharmaceutical drug research and development to share insights into solving the problems they encounter. The BASS book series is initially divided into three volumes addressing: 1) Design of Clinical Trials; 2) Biostatistical Analysis of Clinical Trials; and 3) Pharmaceutical Applications. This book is the second of the 3-volume book series. The topics covered include: Statistical Approaches to the Meta-analysis of Randomized Clinical Trials, Collaborative Targeted Maximum Likelihood Estimation to Assess Causal Effects in Observational Studies, Generalized Tests in Clinical Trials, Discrete Time-to-event and Score-based Methods with Application to Composite Endpoint for Assessing Evidence of Disease Activity-Free , Imputing Missing Data Using a Surrogate Biomarker: Analyzing the Incidence of Endometrial Hyperplasia, Selected Statistical Issues in Patient-reported Outcomes, Network Meta-analysis, Detecting Safety Signals Among Adverse Events in Clinical Trials, Applied Meta-analysis Using R, Treatment of Missing Data in Comparative Effectiveness Research, Causal Estimands: A Common Language for Missing Data, Bayesian Subgroup Analysis with Examples, Statistical Methods in Diagnostic Devices, A Question-Based Approach to the Analysis of Safety Data, Analysis of Two-stage Adaptive Seamless Trial Design, and Multiplicity Problems in Clinical Trials – A Regulatory Perspective.