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| double blind study example: Planning Clinical Research Robert A. Parker, Nancy G. Berman, 2016-10-12 Planning clinical research requires many decisions. The authors of this book explain key decisions with examples showing what works and what does not. |
| double blind study example: Good Research Practice in Non-Clinical Pharmacology and Biomedicine Anton Bespalov, Martin C. Michel, Thomas Steckler, 2020-01-01 This open access book, published under a CC BY 4.0 license in the Pubmed indexed book series Handbook of Experimental Pharmacology, provides up-to-date information on best practice to improve experimental design and quality of research in non-clinical pharmacology and biomedicine. |
| double blind study example: Small Clinical Trials Institute of Medicine, Board on Health Sciences Policy, Committee on Strategies for Small-Number-Participant Clinical Research Trials, 2001-01-01 Clinical trials are used to elucidate the most appropriate preventive, diagnostic, or treatment options for individuals with a given medical condition. Perhaps the most essential feature of a clinical trial is that it aims to use results based on a limited sample of research participants to see if the intervention is safe and effective or if it is comparable to a comparison treatment. Sample size is a crucial component of any clinical trial. A trial with a small number of research participants is more prone to variability and carries a considerable risk of failing to demonstrate the effectiveness of a given intervention when one really is present. This may occur in phase I (safety and pharmacologic profiles), II (pilot efficacy evaluation), and III (extensive assessment of safety and efficacy) trials. Although phase I and II studies may have smaller sample sizes, they usually have adequate statistical power, which is the committee's definition of a large trial. Sometimes a trial with eight participants may have adequate statistical power, statistical power being the probability of rejecting the null hypothesis when the hypothesis is false. Small Clinical Trials assesses the current methodologies and the appropriate situations for the conduct of clinical trials with small sample sizes. This report assesses the published literature on various strategies such as (1) meta-analysis to combine disparate information from several studies including Bayesian techniques as in the confidence profile method and (2) other alternatives such as assessing therapeutic results in a single treated population (e.g., astronauts) by sequentially measuring whether the intervention is falling above or below a preestablished probability outcome range and meeting predesigned specifications as opposed to incremental improvement. |
| double blind study example: Field Trials of Health Interventions Peter G. Smith, Richard H. Morrow, David A. Ross, 2015 This is an open access title available under the terms of a CC BY-NC 4.0 International licence. It is free to read at Oxford Scholarship Online and offered as a free PDF download from OUP and selected open access locations. Before new interventions are released into disease control programmes, it is essential that they are carefully evaluated in field trials'. These may be complex and expensive undertakings, requiring the follow-up of hundreds, or thousands, of individuals, often for long periods. Descriptions of the detailed procedures and methods used in the trials that have been conducted have rarely been published. A consequence of this, individuals planning such trials have few guidelines available and little access to knowledge accumulated previously, other than their own. In this manual, practical issues in trial design and conduct are discussed fully and in sufficient detail, that Field Trials of Health Interventions may be used as a toolbox' by field investigators. It has been compiled by an international group of over 30 authors with direct experience in the design, conduct, and analysis of field trials in low and middle income countries and is based on their accumulated knowledge and experience. Available as an open access book via Oxford Medicine Online, this new edition is a comprehensive revision, incorporating the new developments that have taken place in recent years with respect to trials, including seven new chapters on subjects ranging from trial governance, and preliminary studies to pilot testing. |
| double blind study example: Introductory Business Statistics 2e Alexander Holmes, Barbara Illowsky, Susan Dean, 2023-12-13 Introductory Business Statistics 2e aligns with the topics and objectives of the typical one-semester statistics course for business, economics, and related majors. The text provides detailed and supportive explanations and extensive step-by-step walkthroughs. The author places a significant emphasis on the development and practical application of formulas so that students have a deeper understanding of their interpretation and application of data. Problems and exercises are largely centered on business topics, though other applications are provided in order to increase relevance and showcase the critical role of statistics in a number of fields and real-world contexts. The second edition retains the organization of the original text. Based on extensive feedback from adopters and students, the revision focused on improving currency and relevance, particularly in examples and problems. This is an adaptation of Introductory Business Statistics 2e by OpenStax. You can access the textbook as pdf for free at openstax.org. Minor editorial changes were made to ensure a better ebook reading experience. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution 4.0 International License. |
| double blind study example: Clinical Trials Duolao Wang, Ameet Bakhai, 2006 This book explains statistics specifically for a medically literate audience. Readers gain not only an understanding of the basics of medical statistics, but also a critical insight into how to review and evaluate clinical trial evidence. |
| double blind study example: Sharing Clinical Trial Data Institute of Medicine, Board on Health Sciences Policy, Committee on Strategies for Responsible Sharing of Clinical Trial Data, 2015-04-20 Data sharing can accelerate new discoveries by avoiding duplicative trials, stimulating new ideas for research, and enabling the maximal scientific knowledge and benefits to be gained from the efforts of clinical trial participants and investigators. At the same time, sharing clinical trial data presents risks, burdens, and challenges. These include the need to protect the privacy and honor the consent of clinical trial participants; safeguard the legitimate economic interests of sponsors; and guard against invalid secondary analyses, which could undermine trust in clinical trials or otherwise harm public health. Sharing Clinical Trial Data presents activities and strategies for the responsible sharing of clinical trial data. With the goal of increasing scientific knowledge to lead to better therapies for patients, this book identifies guiding principles and makes recommendations to maximize the benefits and minimize risks. This report offers guidance on the types of clinical trial data available at different points in the process, the points in the process at which each type of data should be shared, methods for sharing data, what groups should have access to data, and future knowledge and infrastructure needs. Responsible sharing of clinical trial data will allow other investigators to replicate published findings and carry out additional analyses, strengthen the evidence base for regulatory and clinical decisions, and increase the scientific knowledge gained from investments by the funders of clinical trials. The recommendations of Sharing Clinical Trial Data will be useful both now and well into the future as improved sharing of data leads to a stronger evidence base for treatment. This book will be of interest to stakeholders across the spectrum of research-from funders, to researchers, to journals, to physicians, and ultimately, to patients. |
| double blind study example: Critical Appraisal of Epidemiological Studies and Clinical Trials Mark Elwood, 2007-02-22 This book presents a logical system of critical appraisal, to allow readers to evaluate studies and to carry out their own studies more effectively. This system emphasizes the central importance of cause and effect relationships. Its great strength is that it is applicable to a wide range of issues, and both to intervention trials and observational studies. This system unifies the often different approaches used in epidemiology, health services research, clinical trials, and evidence-based medicine, starting from a logical consideration of cause and effect. The author's approach to the issues of study design, selection of subjects, bias, confounding, and the place of statistical methods has been praised for its clarity and interest. Systematic reviews, meta-analysis, and the applications of this logic to evidence-based medicine, knowledge-based health care, and health practice and policy are discussed. Current and often controversial examples are used, including screening for prostate cancer, publication bias in psychiatry, public health issues in developing countries, and conflicts between observational studies and randomized trials. Statistical issues are explained clearly without complex mathematics, and the most useful methods are summarized in the appendix. The final chapters give six applications of the critical appraisal of major studies: randomized trials of medical treatment and prevention, a prospective and a retrospective cohort study, a small matched case-control study, and a large case-control study. In these chapters, sections of the original papers are reproduced and the original studies placed in context by a summary of current developments. |
| double blind study example: FDA Approval of New Drugs United States. Food and Drug Administration, 1971 |
| double blind study example: Fundamentals of Clinical Trials Lawrence M. Friedman, Curt Furberg, David L. DeMets, 1998 This classic reference, now updated with the newest applications and results, addresses the fundamentals of such trials based on sound scientific methodology, statistical principles, and years of accumulated experience by the three authors. |
| double blind study example: Encyclopedia of Research Design Neil J. Salkind, 2010-06-22 Comprising more than 500 entries, the Encyclopedia of Research Design explains how to make decisions about research design, undertake research projects in an ethical manner, interpret and draw valid inferences from data, and evaluate experiment design strategies and results. Two additional features carry this encyclopedia far above other works in the field: bibliographic entries devoted to significant articles in the history of research design and reviews of contemporary tools, such as software and statistical procedures, used to analyze results. It covers the spectrum of research design strategies, from material presented in introductory classes to topics necessary in graduate research; it addresses cross- and multidisciplinary research needs, with many examples drawn from the social and behavioral sciences, neurosciences, and biomedical and life sciences; it provides summaries of advantages and disadvantages of often-used strategies; and it uses hundreds of sample tables, figures, and equations based on real-life cases.--Publisher's description. |
| double blind study example: Identifying the Culprit National Research Council, Division of Behavioral and Social Sciences and Education, Committee on Law and Justice, Policy and Global Affairs, Committee on Science, Technology, and Law, Committee on Scientific Approaches to Understanding and Maximizing the Validity and Reliability of Eyewitness Identification in Law Enforcement and the Courts, 2015-01-16 Identifying the Culprit: Assessing Eyewitness Identification makes the case that better data collection and research on eyewitness identification, new law enforcement training protocols, standardized procedures for administering line-ups, and improvements in the handling of eyewitness identification in court can increase the chances that accurate identifications are made. This report explains the science that has emerged during the past 30 years on eyewitness identifications and identifies best practices in eyewitness procedures for the law enforcement community and in the presentation of eyewitness evidence in the courtroom. In order to continue the advancement of eyewitness identification research, the report recommends a focused research agenda. |
| double blind study example: Dietary Supplements United States. Federal Trade Commission. Bureau of Consumer Protection, 1998 |
| double blind study example: Behavioral Clinical Trials for Chronic Diseases Lynda H. Powell, Kenneth E. Freedland, Peter G. Kaufmann, 2021-10-13 This is the first comprehensive guide to the design of behavioral randomized clinical trials (RCT) for chronic diseases. It includes the scientific foundations for behavioral trial methods, problems that have been encountered in past behavioral trials, advances in design that have evolved, and promising trends and opportunities for the future. The value of this book lies in its potential to foster an ability to “speak the language of medicine” through the conduct of high-quality behavioral clinical trials that match the rigor commonly seen in double-blind drug trials. It is relevant for testing any treatment aimed at improving a behavioral, social, psychosocial, environmental, or policy-level risk factor for a chronic disease including, for example, obesity, sedentary behavior, adherence to treatment, psychosocial stress, food deserts, and fragmented care. Outcomes of interest are those that are of clinical significance in the treatment of chronic diseases, including standard risk factors such as cholesterol, blood pressure, and glucose, and clinical outcomes such as hospitalizations, functional limitations, excess morbidity, quality of life, and mortality. This link between behavior and chronic disease requires innovative clinical trial methods not only from the behavioral sciences but also from medicine, epidemiology, and biostatistics. This integration does not exist in any current book, or in any training program, in either the behavioral sciences or medicine. |
| double blind study example: Drug Discovery and Evaluation: Methods in Clinical Pharmacology H.Gerhard Vogel, Jochen Maas, Alexander Gebauer, 2010-12-15 Drug Discovery and Evaluation has become a more and more difficult, expensive and time-consuming process. The effect of a new compound has to be detected by in vitro and in vivo methods of pharmacology. The activity spectrum and the potency compared to existing drugs have to be determined. As these processes can be divided up stepwise we have designed a book series Drug Discovery and Evaluation in the form of a recommendation document. The methods to detect drug targets are described in the first volume of this series Pharmacological Assays comprising classical methods as well as new technologies. Before going to man, the most suitable compound has to be selected by pharmacokinetic studies and experiments in toxicology. These preclinical methods are described in the second volume „Safety and Pharmacokinetic Assays. Only then are first studies in human beings allowed. Special rules are established for Phase I studies. Clinical pharmacokinetics are performed in parallel with human studies on tolerability and therapeutic effects. Special studies according to various populations and different therapeutic indications are necessary. These items are covered in the third volume: „Methods in Clinical Pharmacology. |
| double blind study example: Testing Treatments Imogen Evans, Hazel Thornton, Iain Chalmers, Paul Glasziou, 2011 This work provides a thought-provoking account of how medical treatments can be tested with unbiased or 'fair' trials and explains how patients can work with doctors to achieve this vital goal. It spans the gamut of therapy from mastectomy to thalidomide and explores a vast range of case studies. |
| double blind study example: Neuroscience Trials of the Future National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Health Sciences Policy, Forum on Neuroscience and Nervous System Disorders, 2016-11-07 On March 3-4, 2016, the National Academies of Sciences, Engineering, and Medicine's Forum on Neuroscience and Nervous System Disorders held a workshop in Washington, DC, bringing together key stakeholders to discuss opportunities for improving the integrity, efficiency, and validity of clinical trials for nervous system disorders. Participants in the workshop represented a range of diverse perspectives, including individuals not normally associated with traditional clinical trials. The purpose of this workshop was to generate discussion about not only what is feasible now, but what may be possible with the implementation of cutting-edge technologies in the future. |
| double blind study example: The Social Impact of AIDS in the United States National Research Council, Division of Behavioral and Social Sciences and Education, Commission on Behavioral and Social Sciences and Education, Panel on Monitoring the Social Impact of the AIDS Epidemic, 1993-02-01 Europe's Black Death contributed to the rise of nation states, mercantile economies, and even the Reformation. Will the AIDS epidemic have similar dramatic effects on the social and political landscape of the twenty-first century? This readable volume looks at the impact of AIDS since its emergence and suggests its effects in the next decade, when a million or more Americans will likely die of the disease. The Social Impact of AIDS in the United States addresses some of the most sensitive and controversial issues in the public debate over AIDS. This landmark book explores how AIDS has affected fundamental policies and practices in our major institutions, examining: How America's major religious organizations have dealt with sometimes conflicting values: the imperative of care for the sick versus traditional views of homosexuality and drug use. Hotly debated public health measures, such as HIV antibody testing and screening, tracing of sexual contacts, and quarantine. The potential risk of HIV infection to and from health care workers. How AIDS activists have brought about major change in the way new drugs are brought to the marketplace. The impact of AIDS on community-based organizations, from volunteers caring for individuals to the highly political ACT-UP organization. Coping with HIV infection in prisons. Two case studies shed light on HIV and the family relationship. One reports on some efforts to gain legal recognition for nonmarital relationships, and the other examines foster care programs for newborns with the HIV virus. A case study of New York City details how selected institutions interact to give what may be a picture of AIDS in the future. This clear and comprehensive presentation will be of interest to anyone concerned about AIDS and its impact on the country: health professionals, sociologists, psychologists, advocates for at-risk populations, and interested individuals. |
| double blind study example: Evidence-Based Obstetric Anesthesia Stephen H. Halpern, M. Joanne Douglas, 2008-04-15 This is the first text to systematically review the evidence for obstetric anesthesia and analgesia. Evidence-based practice is now being embraced worldwide as a requirement for all clinicians; in the everyday use of anesthesia and analgesia for childbirth, anesthetists will find this synthesis of the best evidence an invaluable resource to inform their practice. Contributions from anesthetic specialists trained in the skills of systematic reviewing provide a comprehensive and practical guide to best practice in normal and caesarean section childbirth. This book, coming from one of the world’s leading obstetric centers and the cradle of evidence-based medicine, is a much needed addition to the obstetric anesthesia literature. |
| double blind study example: Cochrane Handbook for Systematic Reviews of Interventions Julian P. T. Higgins, Sally Green, 2008-11-24 Healthcare providers, consumers, researchers and policy makers are inundated with unmanageable amounts of information, including evidence from healthcare research. It has become impossible for all to have the time and resources to find, appraise and interpret this evidence and incorporate it into healthcare decisions. Cochrane Reviews respond to this challenge by identifying, appraising and synthesizing research-based evidence and presenting it in a standardized format, published in The Cochrane Library (www.thecochranelibrary.com). The Cochrane Handbook for Systematic Reviews of Interventions contains methodological guidance for the preparation and maintenance of Cochrane intervention reviews. Written in a clear and accessible format, it is the essential manual for all those preparing, maintaining and reading Cochrane reviews. Many of the principles and methods described here are appropriate for systematic reviews applied to other types of research and to systematic reviews of interventions undertaken by others. It is hoped therefore that this book will be invaluable to all those who want to understand the role of systematic reviews, critically appraise published reviews or perform reviews themselves. |
| double blind study example: The Health Effects of Cannabis and Cannabinoids National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Board on Population Health and Public Health Practice, Committee on the Health Effects of Marijuana: An Evidence Review and Research Agenda, 2017-03-31 Significant changes have taken place in the policy landscape surrounding cannabis legalization, production, and use. During the past 20 years, 25 states and the District of Columbia have legalized cannabis and/or cannabidiol (a component of cannabis) for medical conditions or retail sales at the state level and 4 states have legalized both the medical and recreational use of cannabis. These landmark changes in policy have impacted cannabis use patterns and perceived levels of risk. However, despite this changing landscape, evidence regarding the short- and long-term health effects of cannabis use remains elusive. While a myriad of studies have examined cannabis use in all its various forms, often these research conclusions are not appropriately synthesized, translated for, or communicated to policy makers, health care providers, state health officials, or other stakeholders who have been charged with influencing and enacting policies, procedures, and laws related to cannabis use. Unlike other controlled substances such as alcohol or tobacco, no accepted standards for safe use or appropriate dose are available to help guide individuals as they make choices regarding the issues of if, when, where, and how to use cannabis safely and, in regard to therapeutic uses, effectively. Shifting public sentiment, conflicting and impeded scientific research, and legislative battles have fueled the debate about what, if any, harms or benefits can be attributed to the use of cannabis or its derivatives, and this lack of aggregated knowledge has broad public health implications. The Health Effects of Cannabis and Cannabinoids provides a comprehensive review of scientific evidence related to the health effects and potential therapeutic benefits of cannabis. This report provides a research agendaâ€outlining gaps in current knowledge and opportunities for providing additional insight into these issuesâ€that summarizes and prioritizes pressing research needs. |
| double blind study example: Randomization in Clinical Trials William F. Rosenberger, John M. Lachin, 2015-11-23 Praise for the First Edition “All medical statisticians involved in clinical trials should read this book...” - Controlled Clinical Trials Featuring a unique combination of the applied aspects of randomization in clinical trials with a nonparametric approach to inference, Randomization in Clinical Trials: Theory and Practice, Second Edition is the go-to guide for biostatisticians and pharmaceutical industry statisticians. Randomization in Clinical Trials: Theory and Practice, Second Edition features: Discussions on current philosophies, controversies, and new developments in the increasingly important role of randomization techniques in clinical trials A new chapter on covariate-adaptive randomization, including minimization techniques and inference New developments in restricted randomization and an increased focus on computation of randomization tests as opposed to the asymptotic theory of randomization tests Plenty of problem sets, theoretical exercises, and short computer simulations using SAS® to facilitate classroom teaching, simplify the mathematics, and ease readers’ understanding Randomization in Clinical Trials: Theory and Practice, Second Edition is an excellent reference for researchers as well as applied statisticians and biostatisticians. The Second Edition is also an ideal textbook for upper-undergraduate and graduate-level courses in biostatistics and applied statistics. William F. Rosenberger, PhD, is University Professor and Chairman of the Department of Statistics at George Mason University. He is a Fellow of the American Statistical Association and the Institute of Mathematical Statistics, and author of over 80 refereed journal articles, as well as The Theory of Response-Adaptive Randomization in Clinical Trials, also published by Wiley. John M. Lachin, ScD, is Research Professor in the Department of Epidemiology and Biostatistics as well as in the Department of Statistics at The George Washington University. A Fellow of the American Statistical Association and the Society for Clinical Trials, Dr. Lachin is actively involved in coordinating center activities for clinical trials of diabetes. He is the author of Biostatistical Methods: The Assessment of Relative Risks, Second Edition, also published by Wiley. |
| double blind study example: Systematic Reviews in Health Care Matthias Egger, George Davey-Smith, Douglas Altman, 2008-04-15 The second edition of this best-selling book has been thoroughly revised and expanded to reflect the significant changes and advances made in systematic reviewing. New features include discussion on the rationale, meta-analyses of prognostic and diagnostic studies and software, and the use of systematic reviews in practice. |
| double blind study example: A Dictionary of Epidemiology Miquel S. Porta, Sander Greenland, Miguel Hernán, Isabel dos Santos Silva, John M. Last, 2014 This edition is the most updated since its inception, is the essential text for students and professionals working in and around epidemiology or using its methods. It covers subject areas - genetics, clinical epidemiology, public health practice/policy, preventive medicine, health promotion, social sciences and methods for clinical research. |
| double blind study example: Validity and Inter-Rater Reliability Testing of Quality Assessment Instruments U. S. Department of Health and Human Services, Agency for Healthcare Research and Quality, 2013-04-09 The internal validity of a study reflects the extent to which the design and conduct of the study have prevented bias(es). One of the key steps in a systematic review is assessment of a study's internal validity, or potential for bias. This assessment serves to: (1) identify the strengths and limitations of the included studies; (2) investigate, and potentially explain heterogeneity in findings across different studies included in a systematic review; and (3) grade the strength of evidence for a given question. The risk of bias assessment directly informs one of four key domains considered when assessing the strength of evidence. With the increase in the number of published systematic reviews and development of systematic review methodology over the past 15 years, close attention has been paid to the methods for assessing internal validity. Until recently this has been referred to as “quality assessment” or “assessment of methodological quality.” In this context “quality” refers to “the confidence that the trial design, conduct, and analysis has minimized or avoided biases in its treatment comparisons.” To facilitate the assessment of methodological quality, a plethora of tools has emerged. Some of these tools were developed for specific study designs (e.g., randomized controlled trials (RCTs), cohort studies, case-control studies), while others were intended to be applied to a range of designs. The tools often incorporate characteristics that may be associated with bias; however, many tools also contain elements related to reporting (e.g., was the study population described) and design (e.g., was a sample size calculation performed) that are not related to bias. The Cochrane Collaboration recently developed a tool to assess the potential risk of bias in RCTs. The Risk of Bias (ROB) tool was developed to address some of the shortcomings of existing quality assessment instruments, including over-reliance on reporting rather than methods. Several systematic reviews have catalogued and critiqued the numerous tools available to assess methodological quality, or risk of bias of primary studies. In summary, few existing tools have undergone extensive inter-rater reliability or validity testing. Moreover, the focus of much of the tool development or testing that has been done has been on criterion or face validity. Therefore it is unknown whether, or to what extent, the summary assessments based on these tools differentiate between studies with biased and unbiased results (i.e., studies that may over- or underestimate treatment effects). There is a clear need for inter-rater reliability testing of different tools in order to enhance consistency in their application and interpretation across different systematic reviews. Further, validity testing is essential to ensure that the tools being used can identify studies with biased results. Finally, there is a need to determine inter-rater reliability and validity in order to support the uptake and use of individual tools that are recommended by the systematic review community, and specifically the ROB tool within the Evidence-based Practice Center (EPC) Program. In this project we focused on two tools that are commonly used in systematic reviews. The Cochrane ROB tool was designed for RCTs and is the instrument recommended by The Cochrane Collaboration for use in systematic reviews of RCTs. The Newcastle-Ottawa Scale is commonly used for nonrandomized studies, specifically cohort and case-control studies. |
| double blind study example: Modern Epidemiology Kenneth J. Rothman, Sander Greenland, Timothy L. Lash, 2008 The thoroughly revised and updated Third Edition of the acclaimed Modern Epidemiology reflects both the conceptual development of this evolving science and the increasingly focal role that epidemiology plays in dealing with public health and medical problems. Coauthored by three leading epidemiologists, with sixteen additional contributors, this Third Edition is the most comprehensive and cohesive text on the principles and methods of epidemiologic research. The book covers a broad range of concepts and methods, such as basic measures of disease frequency and associations, study design, field methods, threats to validity, and assessing precision. It also covers advanced topics in data analysis such as Bayesian analysis, bias analysis, and hierarchical regression. Chapters examine specific areas of research such as disease surveillance, ecologic studies, social epidemiology, infectious disease epidemiology, genetic and molecular epidemiology, nutritional epidemiology, environmental epidemiology, reproductive epidemiology, and clinical epidemiology. |
| double blind study example: Blinding as a Solution to Bias Christopher T Robertson, Aaron S Kesselheim, 2016-01-30 What information should jurors have during court proceedings to render a just decision? Should politicians know who is donating money to their campaigns? Will scientists draw biased conclusions about drug efficacy when they know more about the patient or study population? The potential for bias in decision-making by physicians, lawyers, politicians, and scientists has been recognized for hundreds of years and drawn attention from media and scholars seeking to understand the role that conflicts of interests and other psychological processes play. However, commonly proposed solutions to biased decision-making, such as transparency (disclosing conflicts) or exclusion (avoiding conflicts) do not directly solve the underlying problem of bias and may have unintended consequences. Robertson and Kesselheim bring together a renowned group of interdisciplinary scholars to consider another way to reduce the risk of biased decision-making: blinding. What are the advantages and limitations of blinding? How can we quantify the biases in unblinded research? Can we develop new ways to blind decision-makers? What are the ethical problems with withholding information from decision-makers in the course of blinding? How can blinding be adapted to legal and scientific procedures and in institutions not previously open to this approach? Fundamentally, these sorts of questions—about who needs to know what—open new doors of inquiry for the design of scientific research studies, regulatory institutions, and courts. The volume surveys the theory, practice, and future of blinding, drawing upon leading authors with a diverse range of methodologies and areas of expertise, including forensic sciences, medicine, law, philosophy, economics, psychology, sociology, and statistics. - Introduces readers to the primary policy issue this book seeks to address: biased decision-making. - Provides a focus on blinding as a solution to bias, which has applicability in many domains. - Traces the development of blinding as a solution to bias, and explores the different ways blinding has been employed. - Includes case studies to explore particular uses of blinding for statisticians, radiologists, and fingerprint examiners, and whether the jurors and judges who rely upon them will value and understand blinding. |
| double blind study example: The Invisible Gorilla Christopher Chabris, Daniel Simons, 2011-06-07 Reading this book will make you less sure of yourself—and that’s a good thing. In The Invisible Gorilla, Christopher Chabris and Daniel Simons, creators of one of psychology’s most famous experiments, use remarkable stories and counterintuitive scientific findings to demonstrate an important truth: Our minds don’t work the way we think they do. We think we see ourselves and the world as they really are, but we’re actually missing a whole lot. Chabris and Simons combine the work of other researchers with their own findings on attention, perception, memory, and reasoning to reveal how faulty intuitions often get us into trouble. In the process, they explain: • Why a company would spend billions to launch a product that its own analysts know will fail • How a police officer could run right past a brutal assault without seeing it • Why award-winning movies are full of editing mistakes • What criminals have in common with chess masters • Why measles and other childhood diseases are making a comeback • Why money managers could learn a lot from weather forecasters Again and again, we think we experience and understand the world as it is, but our thoughts are beset by everyday illusions. We write traffic laws and build criminal cases on the assumption that people will notice when something unusual happens right in front of them. We’re sure we know where we were on 9/11, falsely believing that vivid memories are seared into our minds with perfect fidelity. And as a society, we spend billions on devices to train our brains because we’re continually tempted by the lure of quick fixes and effortless self-improvement. The Invisible Gorilla reveals the myriad ways that our intuitions can deceive us, but it’s much more than a catalog of human failings. Chabris and Simons explain why we succumb to these everyday illusions and what we can do to inoculate ourselves against their effects. Ultimately, the book provides a kind of x-ray vision into our own minds, making it possible to pierce the veil of illusions that clouds our thoughts and to think clearly for perhaps the first time. |
| double blind study example: Double Blind Edward St. Aubyn, 2021-06-01 Double Blind follows three close friends and their circle through a year of extraordinary transformation. Set inLondon, Cap d'Antibes, Big Sur, and a rewilded corner of Sussex, this thrilling, ambitious novel is about the headlong pursuit of knowledge—for the purposes of pleasure, revelation, money, sanity, or survival—and the consequences of fleeing from what we know about others and ourselves. When Olivia meets a new lover just as she is welcoming her best friend, Lucy, back from New York, her dedicated academic life expands precipitously. Her connection to Francis, a committed naturalist living off the grid, is immediate and startling. Eager to involve Lucy in her joy, Olivia introduces the two—but Lucy has received shocking news of her own that binds the trio unusually close. Over the months that follow, Lucy’s boss, Hunter, Olivia’s psychoanalyst parents, and a young man named Sebastian are pulled into the friends’ orbit, and not one of them will emerge unchanged. Expansive, playful, and compassionate, Edward St. Aubyn's Double Blind investigates themes of inheritance, determinism, freedom, consciousness, and the stories we tell about ourselves. It is as compelling about ecology, psychoanalysis, genetics, and neuroscience as it is about love, fear, and courage. Most of all, it is a perfect expression of the interconnections it sets out to examine, and a moving evocation of an imagined world that is deeply intelligent, often tender, curious, and very much alive. |
| double blind study example: Fundamentals of Clinical Trials Lawrence M. Friedman, Curt D. Furberg, David L. DeMets, 2010-09-09 The clinical trial is “the most definitive tool for evaluation of the applicability of clinical research.” It represents “a key research activity with the potential to improve the quality of health care and control costs through careful comparison of alternative treatments” [1]. It has been called on many occasions, “the gold st- dard” against which all other clinical research is measured. Although many clinical trials are of high quality, a careful reader of the medical literature will notice that a large number have deficiencies in design, conduct, analysis, presentation, and/or interpretation of results. Improvements have occurred over the past few decades, but too many trials are still conducted without adequate attention to its fundamental principles. Certainly, numerous studies could have been upgraded if the authors had had a better understanding of the fundamentals. Since the publication of the first edition of this book, a large number of other texts on clinical trials have appeared, most of which are indicated here [2–21]. Several of them, however, discuss only specific issues involved in clinical trials. Additionally, many are no longer current. The purpose of this fourth edition is to update areas in which major progress has been made since the publication of the third edition. We have revised most chapters considerably and added one on ethical issues. |
| double blind study example: Talking Cures and Placebo Effects David A. Jopling, 2008-05-29 Psychodynamic psychotherapy and psychoanalysis have had to defend themselves from a barrage of criticisms throughout their history. In this book David Jopling argues that the changes achieved through therapy are really just functions of placebos that rally the mind's native healing powers. It is a bold new work that delivers yet another blow to Freud and his followers. |
| double blind study example: The Double-bind Dilemma for Women in Leadership , 2007 |
| double blind study example: Statistics Using Technology, Second Edition Kathryn Kozak, 2015-12-12 Statistics With Technology, Second Edition, is an introductory statistics textbook. It uses the TI-83/84 calculator and R, an open source statistical software, for all calculations. Other technology can also be used besides the TI-83/84 calculator and the software R, but these are the ones that are presented in the text. This book presents probability and statistics from a more conceptual approach, and focuses less on computation. Analysis and interpretation of data is more important than how to compute basic statistical values. |
| double blind study example: Pediatric Bipolar Disorder Robert L. Findling, Robert A Kowatch, Robert M. Post, 2002-10-10 Bipolar disorders were once considered rare in children and adolescents. A growing body of scientific evidence now suggests that they may be more prevalent in this group than previously believed. At the same time, the practitioner faces significant clinical challenges in both the assessment processes and also the implementation of a treatment plan. A paucity of treatment manuals and pharmacological algorithms providing practical guidance makes the task of the clinician even more difficult, despite the fact that more is known about the assessment, neurobiology and treatment of children and adolescents with bipolar disorder than ever before. Written by three distinguished experts, this book conveys to clinicians all the information currently available in this area. They review both the neuroscience and also the integration of rational, practical, pharmacological and psychosocial interventions. Based on what is known, a sound approach to the assessment of these youngsters can be developed. Similarly, available evidence allows practitioners to ground their treatment protocols solidly on scientific knowledge. Concise and authoritative, Pediatric Bipolar Disorders will give the reader a practical approach to both the art and science of providing the best possible clinical care to children and adolescents with the disorder. This book is written primarily for clinical psychiatrists, but will also be of interest to non-specialist doctors and other members of the health care team. |
| double blind study example: State of Fear Michael Crichton, 2009-10-13 New York Times bestselling author Michael Crichton delivers another action-packed techo-thriller in State of Fear. When a group of eco-terrorists engage in a global conspiracy to generate weather-related natural disasters, its up to environmental lawyer Peter Evans and his team to uncover the subterfuge. From Tokyo to Los Angeles, from Antarctica to the Solomon Islands, Michael Crichton mixes cutting edge science and action-packed adventure, leading readers on an edge-of-your-seat ride while offering up a thought-provoking commentary on the issue of global warming. A deftly-crafted novel, in true Crichton style, State of Fear is an exciting, stunning tale that not only entertains and educates, but will make you think. |
| double blind study example: Practical Biostatistics Mendel Suchmacher, Mauro Geller, 2012-07-26 Evidence-based medicine aims to apply the best available evidence gained from the scientific method to medical decision making. It is a practice that uses statistical analysis of scientific methods and outcomes to drive further experimentation and diagnosis. The profusion of evidence-based medicine in medical practice and clinical research has produced a need for life scientists and clinical researchers to assimilate biostatistics into their work to meet efficacy and practical standards. Practical Biostatistics provides researchers, medical professionals, and students with a friendly, practical guide to biostatistics. With a detailed outline of implementation steps complemented by a review of important topics, this book can be used as a quick reference or a hands-on guide to effectively incorporate biostatistics in clinical trials. - Customized presentation for biological investigators with examples taken from current clinical trials in multiple disciplines - Clear and concise definitions and examples provide a pragmatic guide to bring clarity to the applications of statistics in improving human health - Addresses the challenge of assimilation of mathematical concepts to better interpret literature, to build stronger studies, to present research effectively, and to improve communication with supporting biostatisticians |
| double blind study example: Common Statistical Methods for Clinical Research with SAS Examples Glenn A. Walker, 2002 This updated edition provides clinical researchers with an invaluable aid for understanding the statistical methods cited most frequently in clinical protocols, statistical analysis plans, clinical and statistical reports, and medical journals. The text is written in a way that takes the non-statistician through each test using examples, yet substantive details are presented that benefit even the most experienced data analysts. |
| double blind study example: Design of Observational Studies Paul R. Rosenbaum, 2020-07-13 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. |
| double blind study example: Critical Appraisal of Epidemiological Studies and Clinical Trials Mark Elwood, 2017-02-23 Since publication of the first three editions of this hugely successful book, systematic methods of critical appraisal have been accepted as central to healthcare provision, both in critical applications and in a wider health services and community perspective. This new edition builds on the work of the previous editions by presenting a fully updated and accessible system of critical appraisal applicable to clinical, epidemiological, and public health studies, and related fields. The book outlines the systematic review process for the establishment of causal effect within single and multiple studies. Focusing primarily on study design, it covers randomized and non-randomized trials, cohort studies, case-control studies, and surveys, showing the presentation of results including person-time and survival analysis, and issues in the selection of subjects. It then describes the process of detection and assessment of selection biases, observation bias, confounding, chance variation, and how to determine internal validity and external validity (generalizability). Statistical methods are presented in an accessible way, illustrating applications to each study design. Positive features of causation including strength, dose-response, and consistency are also discussed. The final chapters provide six examples of critical appraisals of major studies, encompassing randomized trials, prospective and retrospective cohort studies, and case-control studies. Statistical issues are explained clearly without complex mathematics, and the most useful methods are summarized in the appendix, each with a worked example. Each main chapter includes self-test questions, with answers provided, making the book ideally suited to readers with no prior epidemiological or statistical knowledge. Developed over four editions, Critical Appraisal of Epidemiological Studies and Clinical Trials is an invaluable aid to the effective assessment of new studies in epidemiology, public health, research methods, evidence-based methods, clinical medicine, and environmental health; making it essential reading for postgraduates, practitioners, and policymakers in these fields. |
| double blind study example: Planning Clinical Research Robert A. Parker, Nancy G. Berman, 2016-10-12 Planning a clinical study is much more than determining the basic study design. Who will you be studying? How do you plan to recruit your study subjects? How do you plan to retain them in the study? What data do you plan to collect? How will you obtain this data? How will you minimize bias? All these decisions must be consistent with the ethical considerations of studying people. This book teaches how to choose the best design for your question. Drawing on their many years working in clinical research, Nancy G. Berman and Robert A. Parker guide readers through the essential elements of study planning to help get them started. The authors offer numerous examples to illustrate the key decisions needed, describing what works, what does not work, and why. Written specifically for junior investigators beginning their research careers, this guide will also be useful to senior investigators needing to review specific topics. |
What is the difference between float and double? - Stack Overflow
Dec 31, 2021 · Type double, 64 bits long, has a bigger range (*10^+/-308) and 15 digits precision. Type long double is nominally 80 bits, though a given compiler/OS pairing may store it as 12 …
How do I print a double value with full precision using cout?
Dec 17, 2020 · A double is a floating point type, not fixed point. Do not use std::fixed as that fails to print small double as anything but 0.000...000. For large double, it prints many digits, …
Difference between long double and double in C and C++
Apr 22, 2015 · The standard only requires that long double is at least as precise as double, so some compilers will simply treat long double as if it is the same as double. But, on most x86 …
Correct format specifier for double in printf - Stack Overflow
Format %lf is a perfectly correct printf format for double, exactly as you used it. There's nothing wrong with your code. There's nothing wrong with your code. Format %lf in printf was not …
Reading in double values with scanf in c - Stack Overflow
Oct 7, 2017 · I found out that there is a problem with the length of double on 32 bit OS, so that you are forced to use scanf("%lf", &f) to read in a double. No matter what I do, second value is …
decimal vs double! - Which one should I use and when?
Jul 22, 2009 · To clear this up double does not have 16 digits - that is only the number of meaningful digits. Floats are based around exponents in base 2 math - some base 10 …
How to Code Double Quotes via HTML Codes - Stack Overflow
Feb 28, 2013 · I was just curious as to why there needs to be 3 different ways to code a double quotes in html codes, for example. – H. Ferrence Commented Feb 28, 2013 at 12:48
Difference between decimal, float and double in .NET?
Mar 6, 2009 · Double: It is also a floating binary point type variable with double precision and 64 bits size(15-17 significant figures). Double are probably the most generally used data type for …
What is the size of float and double in C and C++? [duplicate]
Aug 27, 2014 · The set of values of the type float is a subset of the set of values of the type double; the set of values of the type double is a subset of the set of values of the type long …
What does the !! (double exclamation mark) operator do in …
The double negation operator !! calculates the truth value of a value. It's actually two operators, where !!x means !(!x), and behaves as follows: If x is a false value, !x is true, and !!x is false. If …
What is the difference between float and double? - Stack Overflow
Dec 31, 2021 · Type double, 64 bits long, has a bigger range (*10^+/-308) and 15 digits precision. Type long double is nominally 80 bits, though a given compiler/OS pairing may store it as 12 …
How do I print a double value with full precision using cout?
Dec 17, 2020 · A double is a floating point type, not fixed point. Do not use std::fixed as that fails to print small double as anything but 0.000...000. For large double, it prints many digits, …
Difference between long double and double in C and C++
Apr 22, 2015 · The standard only requires that long double is at least as precise as double, so some compilers will simply treat long double as if it is the same as double. But, on most x86 …
Correct format specifier for double in printf - Stack Overflow
Format %lf is a perfectly correct printf format for double, exactly as you used it. There's nothing wrong with your code. There's nothing wrong with your code. Format %lf in printf was not …
Reading in double values with scanf in c - Stack Overflow
Oct 7, 2017 · I found out that there is a problem with the length of double on 32 bit OS, so that you are forced to use scanf("%lf", &f) to read in a double. No matter what I do, second value is …
decimal vs double! - Which one should I use and when?
Jul 22, 2009 · To clear this up double does not have 16 digits - that is only the number of meaningful digits. Floats are based around exponents in base 2 math - some base 10 …
How to Code Double Quotes via HTML Codes - Stack Overflow
Feb 28, 2013 · I was just curious as to why there needs to be 3 different ways to code a double quotes in html codes, for example. – H. Ferrence Commented Feb 28, 2013 at 12:48
Difference between decimal, float and double in .NET?
Mar 6, 2009 · Double: It is also a floating binary point type variable with double precision and 64 bits size(15-17 significant figures). Double are probably the most generally used data type for …
What is the size of float and double in C and C++? [duplicate]
Aug 27, 2014 · The set of values of the type float is a subset of the set of values of the type double; the set of values of the type double is a subset of the set of values of the type long …
What does the !! (double exclamation mark) operator do in …
The double negation operator !! calculates the truth value of a value. It's actually two operators, where !!x means !(!x), and behaves as follows: If x is a false value, !x is true, and !!x is false. If …
