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This second edition of Design of Observational Studies is both an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies. An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical or infeasible. Observational studies are common in most fields that study the effects of treatments on people, including medicine, economics, epidemiology, education, psychology, political science and sociology. The quality and strength of evidence provided by an observational study is determined largely by its design. Design of Observational Studies is organized into five parts. Chapters 2, 3, and 5 of Part I cover concisely many of the ideas discussed in Rosenbaum’s Observational Studies (also published by Springer) but in a less technical fashion. Part II discusses the practical aspects of using propensity scores and other tools to create a matched comparison that balances many covariates, and includes an updated chapter on matching in R. In Part III, the concept of design sensitivity is used to appraise the relative ability of competing designs to distinguish treatment effects from biases due to unmeasured covariates. Part IV is new to this edition; it discusses evidence factors and the computerized construction of more than one comparison group. Part V discusses planning the analysis of an observational study, with particular reference to Sir Ronald Fisher’s striking advice for observational studies: "make your theories elaborate." This new edition features updated exploration of causal influence, with four new chapters, a new R package DOS2 designed as a companion for the book, and discussion of several of the latest matching packages for R. In particular, DOS2 allows readers to reproduce many analyses from Design of Observational Studies.
An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical or infeasible. Observational studies are common in most fields that study the effects of treatments on people, including medicine, economics, epidemiology, education, psychology, political science and sociology. The quality and strength of evidence provided by an observational study is determined largely by its design. Design of Observational Studies is both an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies. Design of Observational Studies is divided into four parts. Chapters 2, 3, and 5 of Part I cover concisely, in about one hundred pages, many of the ideas discussed in Rosenbaum’s Observational Studies (also published by Springer) but in a less technical fashion. Part II discusses the practical aspects of using propensity scores and other tools to create a matched comparison that balances many covariates. Part II includes a chapter on matching in R. In Part III, the concept of design sensitivity is used to appraise the relative ability of competing designs to distinguish treatment effects from biases due to unmeasured covariates. Part IV discusses planning the analysis of an observational study, with particular reference to Sir Ronald Fisher’s striking advice for observational studies, "make your theories elaborate." The second edition of his book, Observational Studies, was published by Springer in 2002.
An observational study is an empirical investigation of the effects of treatments, policies, or exposures. It differes from an experiment in that the investigator cannot control the assignments of treatments to subjects. Scientists across a wide range of disciplines undertake such studies, and the aim of this book is to provide a sound statistical account of the principles and methods for the design and analysis of observational studies. Readers are assumed to have a working knowledge of basic probability and statistics, but otherwise the account is reasonably self-contained. Throughout there are extended discussions of actual observational studies to illustrate the ideas discussed. These are drawn from topics as diverse as smoking and lung cancer, lead in children, nuclear weapons testing, and placement programs for students. As a result, many researchers involved in observational studes will find this an invaluable companion to their work.
A daily glass of wine prolongs life—yet alcohol can cause life-threatening cancer. Some say raising the minimum wage will decrease inequality while others say it increases unemployment. Scientists once confidently claimed that hormone replacement therapy reduced the risk of heart disease but now they equally confidently claim it raises that risk. What should we make of this endless barrage of conflicting claims? Observation and Experiment is an introduction to causal inference by one of the field’s leading scholars. An award-winning professor at Wharton, Paul Rosenbaum explains key concepts and methods through lively examples that make abstract principles accessible. He draws his examples from clinical medicine, economics, public health, epidemiology, clinical psychology, and psychiatry to explain how randomized control trials are conceived and designed, how they differ from observational studies, and what techniques are available to mitigate their bias. “Carefully and precisely written...reflecting superb statistical understanding, all communicated with the skill of a master teacher.” —Stephen M. Stigler, author of The Seven Pillars of Statistical Wisdom “An excellent introduction...Well-written and thoughtful...from one of causal inference’s noted experts.” —Journal of the American Statistical Association “Rosenbaum is a gifted expositor...an outstanding introduction to the topic for anyone who is interested in understanding the basic ideas and approaches to causal inference.” —Psychometrika “A very valuable contribution...Highly recommended.” —International Statistical Review
This User’s Guide is a resource for investigators and stakeholders who develop and review observational comparative effectiveness research protocols. It explains how to (1) identify key considerations and best practices for research design; (2) build a protocol based on these standards and best practices; and (3) judge the adequacy and completeness of a protocol. Eleven chapters cover all aspects of research design, including: developing study objectives, defining and refining study questions, addressing the heterogeneity of treatment effect, characterizing exposure, selecting a comparator, defining and measuring outcomes, and identifying optimal data sources. Checklists of guidance and key considerations for protocols are provided at the end of each chapter. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews. More more information, please consult the Agency website: www.effectivehealthcare.ahrq.gov)
Outside of randomized experiments, association does not imply causation, and yet there is nothing defective about our knowledge that smoking causes lung cancer, a conclusion reached in the absence of randomized experimentation with humans. How is that possible? If observed associations do not identify causal effects in observational studies, how can a sequence of such associations become decisive? Two or more associations may each be susceptible to unmeasured biases, yet not susceptible to the same biases. An observational study has two evidence factors if it provides two comparisons susceptible to different biases that may be combined as if from independent studies of different data by different investigators, despite using the same data twice. If the two factors concur, then they may exhibit greater insensitivity to unmeasured biases than either factor exhibits on its own. Replication and Evidence Factors in Observational Studies includes four parts: A concise introduction to causal inference, making the book self-contained Practical examples of evidence factors from the health and social sciences with analyses in R The theory of evidence factors Study design with evidence factors A companion R package evident is available from CRAN.
Cancer screening is a prominent strategy in cancer control in the United States, yet the ability to correctly interpret cancer screening data eludes many researchers, clinicians, and policy makers. This open access primer rectifies that situation by teaching readers, in simple language and with straightforward examples, why and how the population-level cancer burden changes when screening is implemented, and how we assess whether that change is of benefit. This book provides an in-depth look at the many aspects of cancer screening and its assessment, including screening phenomena, performance measures, population-level outcomes, research designs, and other important and timely topics. Concise, accessible, and focused, Assessment of Cancer Screening: A Primer is best suited to those with education or experience in clinical research or public health in the United States - no previous knowledge of cancer screening assessment is necessary. This is the first text dedicated to cancer screening theory and methodology to be published in 20 years.
This is an open access book. The book provides an overview of the state of research in developing countries – Africa, Latin America, and Asia (especially India) and why research and publications are important in these regions. It addresses budding but struggling academics in low and middle-income countries. It is written mainly by senior colleagues who have experienced and recognized the challenges with design, documentation, and publication of health research in the developing world. The book includes short chapters providing insight into planning research at the undergraduate or postgraduate level, issues related to research ethics, and conduct of clinical trials. It also serves as a guide towards establishing a research question and research methodology. It covers important concepts such as writing a paper, the submission process, dealing with rejection and revisions, and covers additional topics such as planning lectures and presentations. The book will be useful for graduates, postgraduates, teachers as well as physicians and practitioners all over the developing world who are interested in academic medicine and wish to do medical research.
"Universal Methods of Design is an immensely useful survey of research and design methods used by today's top practitioners, and will serve as a crucial reference for any designer grappling with really big problems. This book has a place on every designer's bookshelf, including yours!" —David Sherwin, Principal Designer at frog and author of Creative Workshop: 80 Challenges to Sharpen Your Design Skills "Universal Methods of Design is a landmark method book for the field of design. This tidy text compiles and summarizes 100 of the most widely applicable and effective methods of design—research, analysis, and ideation—the methods that every graduate of a design program should know, and every professional designer should employ. Methods are concisely presented, accompanied by information about the origin of the technique, key research supporting the method, and visual examples. Want to know about Card Sorting, or the Elito Method? What about Think-Aloud Protocols? This book has them all and more in readily digestible form. The authors have taken away our excuse for not using the right method for the job, and in so doing have elevated its readers and the field of design. UMOD is an essential resource for designers of all levels and specializations, and should be one of the go-to reference tools found in every designer’s toolbox." —William Lidwell, author of Universal Principles of Design, Lecturer of Industrial Design, University of Houston This comprehensive reference provides a thorough and critical presentation of 100 research methods, synthesis/analysis techniques, and research deliverables for human centered design, delivered in a concise and accessible format perfect for designers, educators, and students. Whether research is already an integral part of a practice or curriculum, or whether it has been unfortunately avoided due to perceived limitations of time, knowledge, or resources, Universal Methods of Design serves as an invaluable compendium of methods that can be easily referenced and utilized by cross-disciplinary teams in nearly any design project. This essential guide: - Dismantles the myth that user research methods are complicated, expensive, and time-consuming - Creates a shared meaning for cross-disciplinary design teams - Illustrates methods with compelling visualizations and case studies - Characterizes each method at a glance - Indicates when methods are best employed to help prioritize appropriate design research strategies Universal Methods of Design distills each method down to its most powerful essence, in a format that will help design teams select and implement the most credible research methods best suited to their design culture within the constraints of their projects.
In simple and non-technical terms, this text illustrates a wide range of techniques and approaches used in social research projects.