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Informatics the study of the use of computer hardware, software, systematic languages, and data manipulation to collect and apply information is united with health care in this new interdisciplinary textbook. It focuses on topics in informatics relevant to all fields of health care, in a textbook format complete with chapter outlines, objectives, key terms, and discussion questions. A unique online supplement complements the book to offer complete, electronic support for both instructors and students. Written by experts in health care informatics, this text provides a comprehensive overview of all the major concepts in informatics, discussing trends and innovative strategies from a contemporary, mainstream perspective.

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Cluster Randomised Trials discusses the design, conduct, and analysis of health trials that randomise groups of individuals to different treatments. It explores the advantages of cluster randomization, with special attention given to evaluating the effects of interventions against infectious diseases.

Avoiding unnecessary mathematical detail, the book covers basic concepts underlying the use of cluster randomisation, such as direct, indirect, and total effects. The authors also present an array of design issues in cluster randomised trials (CRTs), including strategies for minimizing contamination effects, the use of stratification and restricted randomisation to improve balance between treatment arms, special methods for sample size calculation, and alternatives to the simplest two-arm CRT. After covering analytical methods for CRTs, such as regression methods, the authors examine ethical issues, trial monitoring, interim analyses, reporting, and interpretation.

Although the book mainly focuses on medical and public health applications, it shows that the rigorous evidence of intervention effects provided by CRTs has the potential to inform public policy in a wide range of other areas. The book encourages readers to apply the methods to their own trials, reproduce the analyses presented, and explore alternative approaches.

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The third edition of the bestselling Clinical Trials in Oncology provides a concise, nontechnical, and thoroughly up-to-date review of methods and issues related to cancer clinical trials. The authors emphasize the importance of proper study design, analysis, and data management and identify the pitfalls inherent in these processes. In addition, the book has been restructured to have separate chapters and expanded discussions on general clinical trials issues, and issues specific to Phases I, II, and III. New sections cover innovations in Phase I designs, randomized Phase II designs, and overcoming the challenges of array data.

Although this book focuses on cancer trials, the same issues and concepts are important in any clinical setting. As always, the authors use clear, lucid prose and a multitude of real-world examples to convey the principles of successful trials without the need for a strong statistics or mathematics background. Armed with Clinical Trials in Oncology, Third Edition, clinicians and statisticians can avoid the many hazards that can jeopardize the success of a trial.

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Dealing with dynamics of processes that repeat themselves regularly, this revised and updated edition extends the thread from 1980 to the present day, concentrating on areas of interest where there will be much activity in the future. This involves going through spatial biochemical, electrophysiological, and organismic dynamical systems and patterns that were discovered by pursuing the theme of phase singularities introduced in the original book. In particular the work on excitability in cell membranes will be thoroughly updated as will the references throughout the book.

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Providing reliable information on an intervention effect, meta-analysis is a powerful statistical tool for analyzing and combining results from individual studies. Meta-Analysis of Binary Data Using Profile Likelihood focuses on the analysis and modeling of a meta-analysis with individually pooled data (MAIPD). It presents a unifying approach to modeling a treatment effect in a meta-analysis of clinical trials with binary outcomes.

After illustrating the meta-analytic situation of an MAIPD with several examples, the authors introduce the profile likelihood model and extend it to cope with unobserved heterogeneity. They describe elements of log-linear modeling, ways for finding the profile maximum likelihood estimator, and alternative approaches to the profile likelihood method. The authors also discuss how to model covariate information and unobserved heterogeneity simultaneously and use the profile likelihood method to estimate odds ratios. The final chapters look at quantifying heterogeneity in an MAIPD and show how meta-analysis can be applied to the surveillance of scrapie.

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A mere "symbol" of medicine--the sugar pill, saline injection, doctor in a white lab coat--the placebo nonetheless sometimes produces "real" results. Medical science has largely managed its discomfort with this phenomenon by discounting the placebo effect, subtracting it as an impurity in its data through double-blind tests of new treatments and drugs. This book is committed to a different perspective--namely, that the placebo effect is a "real" entity in its own right, one that has much to teach us about how symbols, settings, and human relationships literally get under our skin.

Anne Harrington's introduction and a historical overview by Elaine Shapiro and the late Arthur Shapiro, which open the book, review the place of placebos in the history of medicine, investigate the current surge in interest in them, and probe the methodological difficulties of saying scientifically just what placebos can and cannot do. Combining individual essays with a dialogue among writers from fields as far-flung as cultural anthropology and religion, pharmacology and molecular biology, the book aims to expand our ideas about what the placebo effect is and how it should be seen and studied. At the same time, the book uses the challenges and questions raised by placebo phenomena to initiate a broader interdisciplinary discussion about our nature as cultural animals: animals with minds, brains, and bodies that somehow manage to integrate "biology" and "culture," "mechanism" and "meaning," into a seamless whole.

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More and more frequently, clinical trials include the evaluation of Health-Related Quality of Life (HRQoL), yet many investigators remain unaware of the unique measurement and analysis issues associated with the assessment of HRQoL. At the end of a study, clinicians and statisticians often face challenging and sometimes insurmountable analytic problems.

Design and Analysis of Quality of Life Studies in Clinical Trials details these issues and presents a range of solutions. Written from the author's extensive experience in the field, it focuses on the very specific features of QoL data: its longitudinal nature, multidimensionality, and the problem of missing data. The author uses three real clinical trials throughout her discussions to illustrate practical implementation of the strategies and analytic methods presented.

As Quality of Life becomes an increasingly important aspect of clinical trials, it becomes essential for clinicians, statisticians, and designers of these studies to understand and meet the challenges this kind of data present. In this book, SAS and S-PLUS programs, checklists, numerous figures, and a clear, concise presentation combine to provide readers with the tools and skills they need to successfully design, conduct, analyze, and report their own studies.