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| bias in a study: Bias in Clinical Intervention Research Lise Lotte Gluud, 2005 |
| bias in a study: Applying Quantitative Bias Analysis to Epidemiologic Data Timothy L. Lash, Matthew P. Fox, Aliza K. Fink, 2011-04-14 Bias analysis quantifies the influence of systematic error on an epidemiology study’s estimate of association. The fundamental methods of bias analysis in epi- miology have been well described for decades, yet are seldom applied in published presentations of epidemiologic research. More recent advances in bias analysis, such as probabilistic bias analysis, appear even more rarely. We suspect that there are both supply-side and demand-side explanations for the scarcity of bias analysis. On the demand side, journal reviewers and editors seldom request that authors address systematic error aside from listing them as limitations of their particular study. This listing is often accompanied by explanations for why the limitations should not pose much concern. On the supply side, methods for bias analysis receive little attention in most epidemiology curriculums, are often scattered throughout textbooks or absent from them altogether, and cannot be implemented easily using standard statistical computing software. Our objective in this text is to reduce these supply-side barriers, with the hope that demand for quantitative bias analysis will follow. |
| bias in a study: 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. |
| bias in a study: Foundations of Epidemiology Marit L. Bovbjerg, 2020-10 Foundations of Epidemiology is an open access, introductory epidemiology text intended for students and practitioners in public or allied health fields. It covers epidemiologic thinking, causality, incidence and prevalence, public health surveillance, epidemiologic study designs and why we care about which one is used, measures of association, random error and bias, confounding and effect modification, and screening. Concepts are illustrated with numerous examples drawn from contemporary and historical public health issues. |
| bias in a study: The Oxford Handbook of the Science of Science Communication Kathleen Hall Jamieson, Dan M. Kahan, Dietram Scheufele, 2017 On topics from genetic engineering and mad cow disease to vaccination and climate change, this Handbook draws on the insights of 57 leading science of science communication scholars who explore what social scientists know about how citizens come to understand and act on what is known by science. |
| bias in a study: Finding What Works in Health Care Institute of Medicine, Board on Health Care Services, Committee on Standards for Systematic Reviews of Comparative Effectiveness Research, 2011-07-20 Healthcare decision makers in search of reliable information that compares health interventions increasingly turn to systematic reviews for the best summary of the evidence. Systematic reviews identify, select, assess, and synthesize the findings of similar but separate studies, and can help clarify what is known and not known about the potential benefits and harms of drugs, devices, and other healthcare services. Systematic reviews can be helpful for clinicians who want to integrate research findings into their daily practices, for patients to make well-informed choices about their own care, for professional medical societies and other organizations that develop clinical practice guidelines. Too often systematic reviews are of uncertain or poor quality. There are no universally accepted standards for developing systematic reviews leading to variability in how conflicts of interest and biases are handled, how evidence is appraised, and the overall scientific rigor of the process. In Finding What Works in Health Care the Institute of Medicine (IOM) recommends 21 standards for developing high-quality systematic reviews of comparative effectiveness research. The standards address the entire systematic review process from the initial steps of formulating the topic and building the review team to producing a detailed final report that synthesizes what the evidence shows and where knowledge gaps remain. Finding What Works in Health Care also proposes a framework for improving the quality of the science underpinning systematic reviews. This book will serve as a vital resource for both sponsors and producers of systematic reviews of comparative effectiveness research. |
| bias in a study: A Dictionary of Business Research Methods John Duignan, 2016-02-18 This accessible new dictionary provides clear and authoritative definitions of terms, approaches, and techniques in the area of business research methods. It covers research philosophies including research design and qualitative and quantitative methods, types of data and data collection techniques, and organizing and reporting research finding. It is an invaluable resource for students, academics, and professionals learning about research methods as part of a business degree, and undertaking research in many fields including sociology, psychology, and marketing. |
| bias in a study: Methods of Meta-Analysis John E Hunter, Frank L. Schmidt, 2004-04-07 Covering the most important developments in meta-analysis from 1990 to 2004, this text presents new patterns in research findings as well as updated information on existing topics. |
| bias in a study: Encyclopedia of Survey Research Methods Paul J. Lavrakas, 2008-09-12 To the uninformed, surveys appear to be an easy type of research to design and conduct, but when students and professionals delve deeper, they encounter the vast complexities that the range and practice of survey methods present. To complicate matters, technology has rapidly affected the way surveys can be conducted; today, surveys are conducted via cell phone, the Internet, email, interactive voice response, and other technology-based modes. Thus, students, researchers, and professionals need both a comprehensive understanding of these complexities and a revised set of tools to meet the challenges. In conjunction with top survey researchers around the world and with Nielsen Media Research serving as the corporate sponsor, the Encyclopedia of Survey Research Methods presents state-of-the-art information and methodological examples from the field of survey research. Although there are other how-to guides and references texts on survey research, none is as comprehensive as this Encyclopedia, and none presents the material in such a focused and approachable manner. With more than 600 entries, this resource uses a Total Survey Error perspective that considers all aspects of possible survey error from a cost-benefit standpoint. Key Features Covers all major facets of survey research methodology, from selecting the sample design and the sampling frame, designing and pretesting the questionnaire, data collection, and data coding, to the thorny issues surrounding diminishing response rates, confidentiality, privacy, informed consent and other ethical issues, data weighting, and data analyses Presents a Reader′s Guide to organize entries around themes or specific topics and easily guide users to areas of interest Offers cross-referenced terms, a brief listing of Further Readings, and stable Web site URLs following most entries The Encyclopedia of Survey Research Methods is specifically written to appeal to beginning, intermediate, and advanced students, practitioners, researchers, consultants, and consumers of survey-based information. |
| bias in a study: Publication Bias in Meta-Analysis Hannah R. Rothstein, Alexander J. Sutton, Michael Borenstein, 2005-11-18 Publication bias is the tendency to decide to publish a study based on the results of the study, rather than on the basis of its theoretical or methodological quality. It can arise from selective publication of favorable results, or of statistically significant results. This threatens the validity of conclusions drawn from reviews of published scientific research. Meta-analysis is now used in numerous scientific disciplines, summarizing quantitative evidence from multiple studies. If the literature being synthesised has been affected by publication bias, this in turn biases the meta-analytic results, potentially producing overstated conclusions. Publication Bias in Meta-Analysis examines the different types of publication bias, and presents the methods for estimating and reducing publication bias, or eliminating it altogether. Written by leading experts, adopting a practical and multidisciplinary approach. Provides comprehensive coverage of the topic including: Different types of publication bias, Mechanisms that may induce them, Empirical evidence for their existence, Statistical methods to address them, Ways in which they can be avoided. Features worked examples and common data sets throughout. Explains and compares all available software used for analysing and reducing publication bias. Accompanied by a website featuring software, data sets and further material. Publication Bias in Meta-Analysis adopts an inter-disciplinary approach and will make an excellent reference volume for any researchers and graduate students who conduct systematic reviews or meta-analyses. University and medical libraries, as well as pharmaceutical companies and government regulatory agencies, will also find this invaluable. |
| bias in a study: 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. |
| bias in a study: Cohort Studies in Health Sciences R. Mauricio Barría, 2018 Introductory Chapter: The Contribution of Cohort Studies to Health Sciences. |
| bias in a study: Practical Psychiatric Epidemiology Jayati Das-Munshi, Tamsin Ford, Matthew Hotopf, Martin Prince, Robert Stewart, 2020-04-30 Epidemiology has been defined as the study of the distribution and determinants of health states or events in defined populations and its application to the control of health problems. Psychiatric epidemiology has continued to develop and apply these core principles in relation to mental health and mental disorders. This long-awaited second edition of Practical Psychiatric Epidemiology covers all of the considerable new developments in psychiatric epidemiology that have occurred since the first edition was published. It includes new content on key topics such as life course epidemiology, gene/environment interactions, bioethics, patient and public involvement in research, mixed methods research, new statistical methods, case registers, policy, and implementation. Looking to the future of this rapidly evolving scientific discipline and how it will to respond to the emerging opportunities and challenges posed by 'big data', new technologies, open science and globalisation, this new edition will continue to serve as an invaluable reference for clinicians in practice and in training. It will also be of interest to researchers in mental health and people studying or teaching psychiatric epidemiology at undergraduate or postgraduate level. |
| bias in a study: Concepts of Epidemiology Raj S. Bhopal, 2016 First edition published in 2002. Second edition published in 2008. |
| bias in a study: Games User Research Anders Drachen, Pejman Mirza-Babaei, Lennart E. Nacke, 2018 Games live and die commercially on the player experience. Games User Research is collectively the way we optimise the quality of the user experience (UX) in games, working with all aspects of a game from the mechanics and interface, visuals and art, interaction and progression, making sure every element works in concert and supports the game UX. This means that Games User Research is essential and integral to the production of games and to shape the experience of players. Today, Games User Research stands as the primary pathway to understanding players and how to design, build, and launch games that provide the right game UX. Until now, the knowledge in Games User Research and Game UX has been fragmented and there were no comprehensive, authoritative resources available. This book bridges the current gap of knowledge in Games User Research, building the go-to resource for everyone working with players and games or other interactive entertainment products. It is accessible to those new to Games User Research, while being deeply comprehensive and insightful for even hardened veterans of the game industry. In this book, dozens of veterans share their wisdom and best practices on how to plan user research, obtain the actionable insights from users, conduct user-centred testing, which methods to use when, how platforms influence user research practices, and much, much more. |
| bias in a study: Companion to Women's and Gender Studies Nancy A. Naples, 2020-03-26 A comprehensive overview of the interdisciplinary field of Women's and Gender Studies, featuring original contributions from leading experts from around the world The Companion to Women's and Gender Studies is a comprehensive resource for students and scholars alike, exploring the central concepts, theories, themes, debates, and events in this dynamic field. Contributions from leading scholars and researchers cover a wide range of topics while providing diverse international, postcolonial, intersectional, and interdisciplinary insights. In-depth yet accessible chapters discuss the social construction and reproduction of gender and inequalities in various cultural, social-economic, and political contexts. Thematically-organized chapters explore the development of Women's and Gender Studies as an academic discipline, changes in the field, research directions, and significant scholarship in specific, interrelated disciplines such as science, health, psychology, and economics. Original essays offer fresh perspectives on the mechanisms by which gender intersects with other systems of power and privilege, the relation of androcentric approaches to science and gender bias in research, how feminist activists use media to challenge misrepresentations and inequalities, disparity between men and women in the labor market, how social movements continue to change Women's and Gender Studies, and more. Filling a significant gap in contemporary literature in the field, this volume: Features a broad interdisciplinary and international range of essays Engages with both individual and collective approaches to agency and resistance Addresses topics of intense current interest and debate such as transgender movements, gender-based violence, and gender discrimination policy Includes an overview of shifts in naming, theoretical approaches, and central topics in contemporary Women's and Gender Studies Companion to Women's and Gender Studies is an ideal text for instructors teaching courses in gender, sexuality, and feminist studies, or related disciplines such as psychology, history, education, political science, sociology, and cultural studies, as well as practitioners and policy makers working on issues related to gender and sexuality. |
| bias in a study: Observational Studies Paul R. Rosenbaum, 2013-06-29 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. |
| bias in a study: The Bias That Divides Us Keith E. Stanovich, 2021-08-31 Why we don't live in a post-truth society but rather a myside society: what science tells us about the bias that poisons our politics. In The Bias That Divides Us, psychologist Keith Stanovich argues provocatively that we don't live in a post-truth society, as has been claimed, but rather a myside society. Our problem is not that we are unable to value and respect truth and facts, but that we are unable to agree on commonly accepted truth and facts. We believe that our side knows the truth. Post-truth? That describes the other side. The inevitable result is political polarization. Stanovich shows what science can tell us about myside bias: how common it is, how to avoid it, and what purposes it serves. Stanovich explains that although myside bias is ubiquitous, it is an outlier among cognitive biases. It is unpredictable. Intelligence does not inoculate against it, and myside bias in one domain is not a good indicator of bias shown in any other domain. Stanovich argues that because of its outlier status, myside bias creates a true blind spot among the cognitive elite--those who are high in intelligence, executive functioning, or other valued psychological dispositions. They may consider themselves unbiased and purely rational in their thinking, but in fact they are just as biased as everyone else. Stanovich investigates how this bias blind spot contributes to our current ideologically polarized politics, connecting it to another recent trend: the decline of trust in university research as a disinterested arbiter. |
| bias in a study: Nonresponse in Social Science Surveys National Research Council, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Panel on a Research Agenda for the Future of Social Science Data Collection, 2013-10-26 For many household surveys in the United States, responses rates have been steadily declining for at least the past two decades. A similar decline in survey response can be observed in all wealthy countries. Efforts to raise response rates have used such strategies as monetary incentives or repeated attempts to contact sample members and obtain completed interviews, but these strategies increase the costs of surveys. This review addresses the core issues regarding survey nonresponse. It considers why response rates are declining and what that means for the accuracy of survey results. These trends are of particular concern for the social science community, which is heavily invested in obtaining information from household surveys. The evidence to date makes it apparent that current trends in nonresponse, if not arrested, threaten to undermine the potential of household surveys to elicit information that assists in understanding social and economic issues. The trends also threaten to weaken the validity of inferences drawn from estimates based on those surveys. High nonresponse rates create the potential or risk for bias in estimates and affect survey design, data collection, estimation, and analysis. The survey community is painfully aware of these trends and has responded aggressively to these threats. The interview modes employed by surveys in the public and private sectors have proliferated as new technologies and methods have emerged and matured. To the traditional trio of mail, telephone, and face-to-face surveys have been added interactive voice response (IVR), audio computer-assisted self-interviewing (ACASI), web surveys, and a number of hybrid methods. Similarly, a growing research agenda has emerged in the past decade or so focused on seeking solutions to various aspects of the problem of survey nonresponse; the potential solutions that have been considered range from better training and deployment of interviewers to more use of incentives, better use of the information collected in the data collection, and increased use of auxiliary information from other sources in survey design and data collection. Nonresponse in Social Science Surveys: A Research Agenda also documents the increased use of information collected in the survey process in nonresponse adjustment. |
| bias in a study: The Optimism Bias Tali Sharot, 2011-06-14 Psychologists have long been aware that most people maintain an irrationally positive outlook on life—but why? Turns out, we might be hardwired that way. In this absorbing exploration, Tali Sharot—one of the most innovative neuroscientists at work today—demonstrates that optimism may be crucial to human existence. The Optimism Bias explores how the brain generates hope and what happens when it fails; how the brains of optimists and pessimists differ; why we are terrible at predicting what will make us happy; how emotions strengthen our ability to recollect; how anticipation and dread affect us; how our optimistic illusions affect our financial, professional, and emotional decisions; and more. Drawing on cutting-edge science, The Optimism Bias provides us with startling new insight into the workings of the brain and the major role that optimism plays in determining how we live our lives. |
| bias in a study: Matched Sampling for Causal Effects Donald B. Rubin, 2006-09-04 Matched sampling is often used to help assess the causal effect of some exposure or intervention, typically when randomized experiments are not available or cannot be conducted. This book presents a selection of Donald B. Rubin's research articles on matched sampling, from the early 1970s, when the author was one of the major researchers involved in establishing the field, to recent contributions to this now extremely active area. The articles include fundamental theoretical studies that have become classics, important extensions, and real applications that range from breast cancer treatments to tobacco litigation to studies of criminal tendencies. They are organized into seven parts, each with an introduction by the author that provides historical and personal context and discusses the relevance of the work today. A concluding essay offers advice to investigators designing observational studies. The book provides an accessible introduction to the study of matched sampling and will be an indispensable reference for students and researchers. |
| bias in a study: A Bias Radar for Responsible Policy-Making Lieve Van Woensel, 2020-01-24 Policymakers prepare society for the future and this book provides a practical toolkit for preparing pro-active, future-proof scientific policy advice for them. It explains how to make scientific advisory strategies holistic. It also explains how and where biases, which interfere with the proper functioning of the entire science-policy ecosystem, arise and investigates how emotions and other biases affect the understanding and assessment of scientific evidence. The book advocates explorative foresight, systems thinking, interdisciplinarity, bias awareness and the anticipation of undesirable impacts in policy advising, and it offers practical guidance for them. Written in an accessible style, the book offers provocative reflections on how scientific policy advice should be sensitive to more than scientific evidence. It is both an appealing introductory text for everyone interested in science-based policy and a valuable guide for the experienced scientific adviser and policy scholar. This book is a valuable read for all stakeholders in the scientific advisory ecosystem. Lieve Van Woensel offers concrete methods to bridge the gap between scientific advice and policy making, to assess the possible societal impacts of complex scientific and technological developments, and to support decision-makers’ more strategic understanding of the issues they have to make decisions about. I was privileged to see them proove their value as I worked with Lieve on the pilot project of the Scientific Foresight unit for The European Parliament’s STOA panel.” - Kristel Van der Elst, CEO, The Global Foresight Group; Executive Head, Policy Horizons Canada “A must-read for not only scientific policy advisers, but also those interested in the ethics of scientific advisory processes. Lieve Van Woensel walks readers through a well-structured practical toolkit that bases policy advice on more than scientific evidence by taking into account policies’ potential effects on society and the environment.” - Dr Paul Rübig, Former Member of the European Parliament and former Chair of the Panel for the Future of Science and Technology |
| bias in a study: Blindspot Mahzarin R. Banaji, Anthony G. Greenwald, 2016-08-16 “Accessible and authoritative . . . While we may not have much power to eradicate our own prejudices, we can counteract them. The first step is to turn a hidden bias into a visible one. . . . What if we’re not the magnanimous people we think we are?”—The Washington Post I know my own mind. I am able to assess others in a fair and accurate way. These self-perceptions are challenged by leading psychologists Mahzarin R. Banaji and Anthony G. Greenwald as they explore the hidden biases we all carry from a lifetime of exposure to cultural attitudes about age, gender, race, ethnicity, religion, social class, sexuality, disability status, and nationality. “Blindspot” is the authors’ metaphor for the portion of the mind that houses hidden biases. Writing with simplicity and verve, Banaji and Greenwald question the extent to which our perceptions of social groups—without our awareness or conscious control—shape our likes and dislikes and our judgments about people’s character, abilities, and potential. In Blindspot, the authors reveal hidden biases based on their experience with the Implicit Association Test, a method that has revolutionized the way scientists learn about the human mind and that gives us a glimpse into what lies within the metaphoric blindspot. The title’s “good people” are those of us who strive to align our behavior with our intentions. The aim of Blindspot is to explain the science in plain enough language to help well-intentioned people achieve that alignment. By gaining awareness, we can adapt beliefs and behavior and “outsmart the machine” in our heads so we can be fairer to those around us. Venturing into this book is an invitation to understand our own minds. Brilliant, authoritative, and utterly accessible, Blindspot is a book that will challenge and change readers for years to come. Praise for Blindspot “Conversational . . . easy to read, and best of all, it has the potential, at least, to change the way you think about yourself.”—Leonard Mlodinow, The New York Review of Books “Banaji and Greenwald deserve a major award for writing such a lively and engaging book that conveys an important message: Mental processes that we are not aware of can affect what we think and what we do. Blindspot is one of the most illuminating books ever written on this topic.”—Elizabeth F. Loftus, Ph.D., distinguished professor, University of California, Irvine; past president, Association for Psychological Science; author of Eyewitness Testimony |
| bias in a study: Handbook for Clinical Research Flora Hammond, MD, James F. Malec, Todd Nick, Ralph Buschbacher, MD, 2014-08-26 With over 80 information-packed chapters, Handbook for Clinical Research delivers the practical insights and expert tips necessary for successful research design, analysis, and implementation. Using clear language and an accessible bullet point format, the authors present the knowledge and expertise developed over time and traditionally shared from mentor to mentee and colleague to colleague. Organized for quick access to key topics and replete with practical examples, the book describes a variety of research designs and statistical methods and explains how to choose the best design for a particular project. Research implementation, including regulatory issues and grant writing, is also covered. The book opens with a section on the basics of research design, discussing the many ways in which studies can be organized, executed, and evaluated. The second section is devoted to statistics and explains how to choose the correct statistical approach and reviews the varieties of data types, descriptive and inferential statistics, methods for demonstrating associations, hypothesis testing and prediction, specialized methods, and considerations in epidemiological studies and measure construction. The third section covers implementation, including how to develop a grant application step by step, the project budget, and the nuts and bolts of the timely and successful completion of a research project and documentation of findings: procedural manuals and case report forms collecting, managing and securing data operational structure and ongoing monitoring and evaluation and ethical and regulatory concerns in research with human subjects. With a concise presentation of the essentials for successful research, the Handbook for Clinical Research is a valuable addition to the library of any student, research professional, or clinician interested in expanding the knowledge base of his or her field. Key Features: Delivers the essential elements, practical insights, and trade secrets for ensuring successful research design, analysis, and implementation Presents the nuts and bolts of statistical analysis Organized for quick access to a wealth of information Replete with practical examples of successful research designs Û from single case designs to meta-analysis - and how to achieve them Addresses research implementation including regulatory issues and grant writing |
| bias in a study: Applied Thematic Analysis Greg Guest, Kathleen M. MacQueen, Emily E. Namey, 2012 This book provides step-by-step instructions on how to analyze text generated from in-depth interviews and focus groups, relating predominantly to applied qualitative studies. The book covers all aspects of the qualitative data analysis process, employing a phenomenological approach which has a primary aim of describing the experiences and perceptions of research participants. Similar to Grounded Theory, the authors' approach is inductive, content-driven, and searches for themes within textual data. |
| bias in a study: Anthropic Bias Nick Bostrom, 2013-10-11 Anthropic Bias explores how to reason when you suspect that your evidence is biased by observation selection effects--that is, evidence that has been filtered by the precondition that there be some suitably positioned observer to have the evidence. This conundrum--sometimes alluded to as the anthropic principle, self-locating belief, or indexical information--turns out to be a surprisingly perplexing and intellectually stimulating challenge, one abounding with important implications for many areas in science and philosophy. There are the philosophical thought experiments and paradoxes: the Doomsday Argument; Sleeping Beauty; the Presumptuous Philosopher; Adam & Eve; the Absent-Minded Driver; the Shooting Room. And there are the applications in contemporary science: cosmology (How many universes are there?, Why does the universe appear fine-tuned for life?); evolutionary theory (How improbable was the evolution of intelligent life on our planet?); the problem of time's arrow (Can it be given a thermodynamic explanation?); quantum physics (How can the many-worlds theory be tested?); game-theory problems with imperfect recall (How to model them?); even traffic analysis (Why is the 'next lane' faster?). Anthropic Bias argues that the same principles are at work across all these domains. And it offers a synthesis: a mathematically explicit theory of observation selection effects that attempts to meet scientific needs while steering clear of philosophical paradox. |
| bias in a study: 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. |
| bias in a study: Conflict of Interest in Medical Research, Education, and Practice Institute of Medicine, Board on Health Sciences Policy, Committee on Conflict of Interest in Medical Research, Education, and Practice, 2009-09-16 Collaborations of physicians and researchers with industry can provide valuable benefits to society, particularly in the translation of basic scientific discoveries to new therapies and products. Recent reports and news stories have, however, documented disturbing examples of relationships and practices that put at risk the integrity of medical research, the objectivity of professional education, the quality of patient care, the soundness of clinical practice guidelines, and the public's trust in medicine. Conflict of Interest in Medical Research, Education, and Practice provides a comprehensive look at conflict of interest in medicine. It offers principles to inform the design of policies to identify, limit, and manage conflicts of interest without damaging constructive collaboration with industry. It calls for both short-term actions and long-term commitments by institutions and individuals, including leaders of academic medical centers, professional societies, patient advocacy groups, government agencies, and drug, device, and pharmaceutical companies. Failure of the medical community to take convincing action on conflicts of interest invites additional legislative or regulatory measures that may be overly broad or unduly burdensome. Conflict of Interest in Medical Research, Education, and Practice makes several recommendations for strengthening conflict of interest policies and curbing relationships that create risks with little benefit. The book will serve as an invaluable resource for individuals and organizations committed to high ethical standards in all realms of medicine. |
| bias in a study: Research Design in Political Science T. Gschwend, F. Schimmelfennig, 2007-10-23 When embarking on a new research project students face the same core research design issues. This volume provides readers with practical guidelines for both qualitative and quantitative designs, discusses the typical trade-offs involved in choosing them and is rich in examples from actual research. |
| bias in a study: Quality Assurance Implementation in Research Labs Akshay Anand, 2021-08-17 This book is a comprehensive and timely compilation of strategy, methods, and implementation of a proof of concept modified quality module of Good Laboratory Practices (GLP). This text provides a historical overview of GLP and related standards of quality assurance practices in clinical testing laboratories as well as basic research settings. It specifically discusses the need and challenges in audit, documentation, and strategies for its implications in system-dependent productivity striving research laboratories. It also describes the importance of periodic training of study directors as well as the scholars for standardization in research processes. This book describes different documents required at various time points of a successful Ph.D and post-doc tenure along with faculty training besides entire lab establishments. Various other areas including academic social responsibility and quality assurance in the developing world, lab orientations, and communication, digitization in data accuracy, auditability and back traceability have also been discussed. This book will be a preferred source for principal investigators, research scholars, and industrial research centers globally. From the foreword by Ratan Tata, India “This book will be a guide for students and professionals alike in quality assurance practices related to clinical research labs. The historical research and fundamental principles make it a good tool in clinical research environments. The country has a great need for such a compilation in order to increase the application of domestic capabilities and technology” |
| bias in a study: Developing a Protocol for Observational Comparative Effectiveness Research: A User's Guide Agency for Health Care Research and Quality (U.S.), 2013-02-21 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) |
| bias in a study: Doing Meta-Analysis with R Mathias Harrer, Pim Cuijpers, Toshi A. Furukawa, David D. Ebert, 2021-09-15 Doing Meta-Analysis with R: A Hands-On Guide serves as an accessible introduction on how meta-analyses can be conducted in R. Essential steps for meta-analysis are covered, including calculation and pooling of outcome measures, forest plots, heterogeneity diagnostics, subgroup analyses, meta-regression, methods to control for publication bias, risk of bias assessments and plotting tools. Advanced but highly relevant topics such as network meta-analysis, multi-three-level meta-analyses, Bayesian meta-analysis approaches and SEM meta-analysis are also covered. A companion R package, dmetar, is introduced at the beginning of the guide. It contains data sets and several helper functions for the meta and metafor package used in the guide. The programming and statistical background covered in the book are kept at a non-expert level, making the book widely accessible. Features • Contains two introductory chapters on how to set up an R environment and do basic imports/manipulations of meta-analysis data, including exercises • Describes statistical concepts clearly and concisely before applying them in R • Includes step-by-step guidance through the coding required to perform meta-analyses, and a companion R package for the book |
| bias in a study: Epidemiology in Medicine Julie E. Buring, 1987 Harvard Medical School, Boston. Textbook for medical and public health students. |
| bias in a study: Comparative Effectiveness Review Methods U. S. Department of Health and Human Services, Agency for Healthcare Research and Quality, 2013-05-17 The Agency for Healthcare Research and Quality (AHRQ) commissioned the RTI International–University of North Carolina at Chapel Hill (RTI-UNC) Evidence-based Practice Center (EPC) to explore how systematic review groups have dealt with clinical heterogeneity and to seek out best practices for addressing clinical heterogeneity in systematic reviews (SRs) and comparative effectiveness reviews (CERs). Such best practices, to the extent they exist, may enable AHRQ's EPCs to address critiques from patients, clinicians, policymakers, and other proponents of health care about the extent to which “average” estimates of the benefits and harms of health care interventions apply to individual patients or to small groups of patients sharing similar characteristics. Such users of reviews often assert that EPC reviews typically focus on broad populations and, as a result, often lack information relevant to patient subgroups that are of particular concern to them. More important, even when EPCs evaluate literature on homogeneous groups, there may be varying individual treatment for no apparent reason, indicating that average treatment effect does not point to the best treatment for any given individual. Thus, the health care community is looking for better ways to develop information that may foster better medical care at a “personal” or “individual” level. To address our charge for this methods project, the EPC set out to answer six key questions (KQ). Key questions for methods report on clinical heterogeneity include: 1. What is clinical heterogeneity? a. How has it been defined by various groups? b. How is it distinct from statistical heterogeneity? c. How does it fit with other issues that have been addressed by the AHRQ Methods Manual for CERs? 2. How have systematic reviews dealt with clinical heterogeneity in the key questions? a. What questions have been asked? b. How have they pre-identified population subgroups with common clinical characteristics that modify their intervention-outcome association? c. What are best practices in key questions and how these subgroups have been identified? 3. How have systematic reviews dealt with clinical heterogeneity in the review process? a. What do guidance documents of various systematic review groups recommend? b. How have EPCs handled clinical heterogeneity in their reviews? c. What are best practices in searching for and interpreting results for particular subgroups with common clinical characteristics that may modify their intervention-outcome association? 4. What are critiques in how systematic reviews handle clinical heterogeneity? a. What are critiques from specific reviews (peer and public) on how EPCs handled clinical heterogeneity? b. What general critiques (in the literature) have been made against how systematic reviews handle clinical heterogeneity? 5. What evidence is there to support how to best address clinical heterogeneity in a systematic review? 6. What questions should an EPC work group on clinical heterogeneity address? Heterogeneity (of any type) in EPC reviews is important because its appearance suggests that included studies differed on one or more dimensions such as patient demographics, study designs, coexisting conditions, or other factors. EPCs then need to clarify for clinical and other audiences, collectively referred to as stakeholders, what are the potential causes of the heterogeneity in their results. This will allow the stakeholders to understand whether and to what degree they can apply this information to their own patients or constituents. Of greatest importance for this project was clinical heterogeneity, which we define as the variation in study population characteristics, coexisting conditions, cointerventions, and outcomes evaluated across studies included in an SR or CER that may influence or modify the magnitude of the intervention measure of effect (e.g., odds ratio, risk ratio, risk difference). |
| bias in a study: Methods in Social Epidemiology J. Michael Oakes, Jay S. Kaufman, 2006-05-11 Social epidemiology is the study of how social interactions—social norms, laws, institutions, conventia, social conditions and behavior—affect the health of populations. This practical, comprehensive introduction to methods in social epidemiology is written by experts in the field. It is perfectly timed for the growth in interest among those in public health, community health, preventive medicine, sociology, political science, social work, and other areas of social research. Topics covered are: Introduction: Advancing Methods in Social Epidemiology The History of Methods of Social Epidemilogy to 1965 Indicators of Socioeconomic Position Measuring and Analyzing 'Race' Racism and Racial Discrimination Measuring Poverty Measuring Health Inequalities A Conceptual Framework for Measuring Segregation and its Association with Population Outcomes Measures of Residential Community Contexts Using Census Data to Approximate Neighborhood Effects Community-based Participatory Research: Rationale and Relevance for Social Epidemiology Network Methods in Social Epidemiology Identifying Social Interactions: A Review, Multilevel Studies Experimental Social Epidemiology: Controlled Community Trials Propensity Score Matching Methods for Social Epidemiology Natural Experiments and Instrumental Variable Analyses in Social Epidemiology and Using Causal Diagrams to Understand Common Problems in Social Epidemiology. Publication of this highly informative textbook clearly reflects the coming of age of many social epidemiology methods, the importance of which rests on their potential contribution to significantly improving the effectiveness of the population-based approach to prevention. This book should be of great interest not only to more advanced epidemiology students but also to epidemiologists in general, particularly those concerned with health policy and the translation of epidemiologic findings into public health practice. The cause of achieving a ‘more complete’ epidemiology envisaged by the editors has been significantly advanced by this excellent textbook. —Moyses Szklo, professor of epidemiology and editor-in-chief, American Journal of Epidemiology, Johns Hopkins University Social epidemiology is a comparatively new field of inquiry that seeks to describe and explain the social and geographic distribution of health and of the determinants of health. This book considers the major methodological challenges facing this important field. Its chapters, written by experts in a variety of disciplines, are most often authoritative, typically provocative, and often debatable, but always worth reading. —Stephen W. Raudenbush, Lewis-Sebring Distinguished Service Professor, Department of Sociology, University of Chicago The roadmap for a new generation of social epidemiologists. The publication of this treatise is a significant event in the history of the discipline. —Ichiro Kawachi, professor of social epidemiology, Department of Society, Human Development, and Health, Harvard University Methods in Social Epidemiology not only illuminates the difficult questions that future generations of social epidemiologists must ask, it also identifies the paths they must boldly travel in the pursuit of answers, if this exciting interdisciplinary science is to realize its full potential. This beautifully edited volume appears at just the right moment to exert a profound influence on the field. —Sherman A. James, Susan B. King Professor of Public Policy Studies, professor of Community and Family Medicine, professor of African-American Studies, Duke University |
| bias in a study: SAGE Research Methods Foundations Paul Anthony Atkinson, Sara Delamont, Richard A. Williams, Alexandru Cernat, Joseph Sakshaug, 2021-05-05 |
| bias in a study: Laziness Does Not Exist Devon Price, 2021-01-05 From social psychologist Dr. Devon Price, a conversational, stirring call to “a better, more human way to live” (Cal Newport, New York Times bestselling author) that examines the “laziness lie”—which falsely tells us we are not working or learning hard enough. Extra-curricular activities. Honors classes. 60-hour work weeks. Side hustles. Like many Americans, Dr. Devon Price believed that productivity was the best way to measure self-worth. Price was an overachiever from the start, graduating from both college and graduate school early, but that success came at a cost. After Price was diagnosed with a severe case of anemia and heart complications from overexertion, they were forced to examine the darker side of all this productivity. Laziness Does Not Exist explores the psychological underpinnings of the “laziness lie,” including its origins from the Puritans and how it has continued to proliferate as digital work tools have blurred the boundaries between work and life. Using in-depth research, Price explains that people today do far more work than nearly any other humans in history yet most of us often still feel we are not doing enough. Filled with practical and accessible advice for overcoming society’s pressure to do more, and featuring interviews with researchers, consultants, and experiences from real people drowning in too much work, Laziness Does Not Exist “is the book we all need right now” (Caroline Dooner, author of The F*ck It Diet). |
| bias in a study: Knowing What Works in Health Care Institute of Medicine, Board on Health Care Services, Committee on Reviewing Evidence to Identify Highly Effective Clinical Services, 2008-05-29 There is currently heightened interest in optimizing health care through the generation of new knowledge on the effectiveness of health care services. The United States must substantially strengthen its capacity for assessing evidence on what is known and not known about what works in health care. Even the most sophisticated clinicians and consumers struggle to learn which care is appropriate and under what circumstances. Knowing What Works in Health Care looks at the three fundamental health care issues in the United States-setting priorities for evidence assessment, assessing evidence (systematic review), and developing evidence-based clinical practice guidelines-and how each of these contributes to the end goal of effective, practical health care systems. This book provides an overall vision and roadmap for improving how the nation uses scientific evidence to identify the most effective clinical services. Knowing What Works in Health Care gives private and public sector firms, consumers, health care professionals, benefit administrators, and others the authoritative, independent information required for making essential informed health care decisions. |
| bias in a study: 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. |
| bias in a study: Randomized Controlled Trials Alehandro R. Jadad, Murray W. Enkin, 2007-07-23 Randomized controlled trials are one of the most powerful and revolutionary tools of research. This book is a convenient and accessible description of the underlying principles and practice of randomized controlled trials and their role in clinical decision-making. Structured in a jargon-free question-and-answer format, each chapter provides concise and understandable information on a different aspect of randomized controlled trials, from the basics of trial design and terminology to the interpretation of results and their use in driving evidence-based medicine. The authors end each chapter with their musings, going beyond the evidence or citations, and sometimes even beyond orthodox correctness to share their thoughts and concerns about different aspects of randomized controlled trials, and their role within the health system. Updated to include insights from the last decade, this second edition challenges over-reliance on randomized controlled trials by debating their strengths and limitations and discussing their optimal use in modern healthcare. It also includes a new and increasingly relevant chapter on the ethics of randomized trials. World renowned writers and thinkers Drs Jadad and Enkin bring you this invaluable book for busy health professionals who wish to understand the theory of randomized controlled trials and their influence on clinical, research or policy decisions. |
机器学习中的 Bias（偏差）、Error（误差）、Variance（方差） …
首先明确一点，Bias和Variance是针对Generalization（一般化，泛化）来说的。. 在机器学习中，我们用训练数据集去训练（学习）一个model（模型），通常的做法是定义一个Loss …
神经网络中的偏置（bias）究竟有什么用？ - 知乎
神经网络中的偏置（bias）究竟有什么用？ 最近写了一下模式识别的作业，简单的用python实现了一个三层神经网络，发现不加偏置的话，网络的训练精度一直不能够提升，加了偏执之后反而 …
偏差——bias与deviation的联系/区别？ - 知乎
各位同学，你们有没有想过‘偏见’在英语中是怎么说的？没错，答案就是'bias'！而且，我们这次还结合了一款超酷的桌面背单词软件，让你在学习单词的同时，也能感受到科技的魅
英文中prejudice和bias的区别? - 知乎
Bias：Bias is a tendency to prefer one person or thing to another, and to favour that person or thing. 可见 bias 所表示的意思是“偏爱”，其本质是一种喜好，而非厌恶，所以没有偏见的意思。
sci投稿Declaration of interest怎么写? - 知乎
正在写SCI的小伙伴看到这篇回答有福了！作为一个在硕士阶段发表了4篇SCI（一区×2，二区×2）的人，本回答就好好给你唠唠究竟该如何撰写Declaration of interest利益声明部分。
确认偏误是什么？如何系统地克服确认偏误？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业 …
Linear classifier 里的 bias 有什么用？ - 知乎
Oct 27, 2015 · 你想象一下一维的情况，如果有两个点 -1 是负类， -2 是正类。如果没有bias，你的分类边界只能是过远点的一条垂直线，没法区分出这两个类别，bias给你提供了在特征空间上 …
选择性偏差（selection bias）指的是什么？ - 知乎
选择性偏差指的是在研究过程中因样本选择的非随机性而导致得到的结论存在偏差，包括自选择偏差（self-selection bias）和样本选择偏差（sample-selection bias）。消除选择性偏差，我们 …
哪里有标准的机器学习术语(翻译)对照表？ - 知乎
预测偏差 (prediction bias) 一种值，用于表明预测平均值与数据集中标签的平均值相差有多大。 预训练模型 (pre-trained model) 已经过训练的模型或模型组件（例如嵌套）。有时，您需要将预 …
如何理解Adam算法(Adaptive Moment Estimation)？ - 知乎
完整的Adam更新算法也包含了一个偏置（bias）矫正机制，因为m,v两个矩阵初始为0，在没有完全热身之前存在偏差，需要采取一些补偿措施。 不同最优化方法效果
机器学习中的 Bias（偏差）、Error（误差）、Variance（方差）有 …
首先明确一点，Bias和Variance是针对Generalization（一般化，泛化）来说的。. 在机器学习中，我们用训练数据集去训练（学习）一个model（模型），通常的做法是定义一个Loss function（误差函 …
神经网络中的偏置（bias）究竟有什么用？ - 知乎
神经网络中的偏置（bias）究竟有什么用？ 最近写了一下模式识别的作业，简单的用python实现了一个三层神经网络，发现不加偏置的话，网络的训练精度一直不能够提升，加了偏执之后反而训练精度提 …
偏差——bias与deviation的联系/区别？ - 知乎
各位同学，你们有没有想过‘偏见’在英语中是怎么说的？没错，答案就是'bias'！而且，我们这次还结合了一款超酷的桌面背单词软件，让你在学习单词的同时，也能感受到科技的魅
英文中prejudice和bias的区别? - 知乎
Bias：Bias is a tendency to prefer one person or thing to another, and to favour that person or thing. 可见 bias 所表示的意思是“偏爱”，其本质是一种喜好，而非厌恶，所以没有偏见的意思。
sci投稿Declaration of interest怎么写? - 知乎
正在写SCI的小伙伴看到这篇回答有福了！作为一个在硕士阶段发表了4篇SCI（一区×2，二区×2）的人，本回答就好好给你唠唠究竟该如何撰写Declaration of interest利益声明部分。
确认偏误是什么？如何系统地克服确认偏误？ - 知乎
知乎，中文互联网高质量的问答社区和创作者聚集的原创内容平台，于 2011 年 1 月正式上线，以「让人们更好的分享知识、经验和见解，找到自己的解答」为品牌使命。知乎凭借认真、专业、友善的社区 …
Linear classifier 里的 bias 有什么用？ - 知乎
Oct 27, 2015 · 你想象一下一维的情况，如果有两个点 -1 是负类， -2 是正类。如果没有bias，你的分类边界只能是过远点的一条垂直线，没法区分出这两个类别，bias给你提供了在特征空间上平移的自由 …
选择性偏差（selection bias）指的是什么？ - 知乎
选择性偏差指的是在研究过程中因样本选择的非随机性而导致得到的结论存在偏差，包括自选择偏差（self-selection bias）和样本选择偏差（sample-selection bias）。消除选择性偏差，我们才能拨云 …
哪里有标准的机器学习术语(翻译)对照表？ - 知乎
预测偏差 (prediction bias) 一种值，用于表明预测平均值与数据集中标签的平均值相差有多大。 预训练模型 (pre-trained model) 已经过训练的模型或模型组件（例如嵌套）。有时，您需要将预训练的嵌 …
如何理解Adam算法(Adaptive Moment Estimation)？ - 知乎
完整的Adam更新算法也包含了一个偏置（bias）矫正机制，因为m,v两个矩阵初始为0，在没有完全热身之前存在偏差，需要采取一些补偿措施。 不同最优化方法效果
