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| can causation be determined from an observational study: Commercial Motor Vehicle Driver Fatigue, Long-Term Health, and Highway Safety National Academies of Sciences, Engineering, and Medicine, Transportation Research Board, Division of Behavioral and Social Sciences and Education, Board on Human-Systems Integration, Committee on National Statistics, Panel on Research Methodologies and Statistical Approaches to Understanding Driver Fatigue Factors in Motor Carrier Safety and Driver Health, 2016-09-12 There are approximately 4,000 fatalities in crashes involving trucks and buses in the United States each year. Though estimates are wide-ranging, possibly 10 to 20 percent of these crashes might have involved fatigued drivers. The stresses associated with their particular jobs (irregular schedules, etc.) and the lifestyle that many truck and bus drivers lead, puts them at substantial risk for insufficient sleep and for developing short- and long-term health problems. Commercial Motor Vehicle Driver Fatigue, Long-Term Health and Highway Safety assesses the state of knowledge about the relationship of such factors as hours of driving, hours on duty, and periods of rest to the fatigue experienced by truck and bus drivers while driving and the implications for the safe operation of their vehicles. This report evaluates the relationship of these factors to drivers' health over the longer term, and identifies improvements in data and research methods that can lead to better understanding in both areas. |
| can causation be determined from an observational study: 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. |
| can causation be determined from an observational study: Guiding Principles for Developing Dietary Reference Intakes Based on Chronic Disease National Academies of Sciences, Engineering, and Medicine, Health and Medicine Division, Food and Nutrition Board, Committee on the Development of Guiding Principles for the Inclusion of Chronic Disease Endpoints in Future Dietary Reference Intakes, 2017-12-21 Since 1938 and 1941, nutrient intake recommendations have been issued to the public in Canada and the United States, respectively. Currently defined as the Dietary Reference Intakes (DRIs), these values are a set of standards established by consensus committees under the National Academies of Sciences, Engineering, and Medicine and used for planning and assessing diets of apparently healthy individuals and groups. In 2015, a multidisciplinary working group sponsored by the Canadian and U.S. government DRI steering committees convened to identify key scientific challenges encountered in the use of chronic disease endpoints to establish DRI values. Their report, Options for Basing Dietary Reference Intakes (DRIs) on Chronic Disease: Report from a Joint US-/Canadian-Sponsored Working Group, outlined and proposed ways to address conceptual and methodological challenges related to the work of future DRI Committees. This report assesses the options presented in the previous report and determines guiding principles for including chronic disease endpoints for food substances that will be used by future National Academies committees in establishing DRIs. |
| can causation be determined from an observational study: Causal Inference Miquel A. Hernan, James M. Robins, 2019-07-07 The application of causal inference methods is growing exponentially in fields that deal with observational data. Written by pioneers in the field, this practical book presents an authoritative yet accessible overview of the methods and applications of causal inference. With a wide range of detailed, worked examples using real epidemiologic data as well as software for replicating the analyses, the text provides a thorough introduction to the basics of the theory for non-time-varying treatments and the generalization to complex longitudinal data. |
| can causation be determined from an observational study: Observation and Experiment Paul Rosenbaum, 2017-08-14 A daily glass of wine prolongs life—yet alcohol can cause life-threatening cancer. Some say raising the minimum wage will decrease inequality while others say it increases unemployment. Scientists once confidently claimed that hormone replacement therapy reduced the risk of heart disease but now they equally confidently claim it raises that risk. What should we make of this endless barrage of conflicting claims? Observation and Experiment is an introduction to causal inference by one of the field’s leading scholars. An award-winning professor at Wharton, Paul Rosenbaum explains key concepts and methods through lively examples that make abstract principles accessible. He draws his examples from clinical medicine, economics, public health, epidemiology, clinical psychology, and psychiatry to explain how randomized control trials are conceived and designed, how they differ from observational studies, and what techniques are available to mitigate their bias. “Carefully and precisely written...reflecting superb statistical understanding, all communicated with the skill of a master teacher.” —Stephen M. Stigler, author of The Seven Pillars of Statistical Wisdom “An excellent introduction...Well-written and thoughtful...from one of causal inference’s noted experts.” —Journal of the American Statistical Association “Rosenbaum is a gifted expositor...an outstanding introduction to the topic for anyone who is interested in understanding the basic ideas and approaches to causal inference.” —Psychometrika “A very valuable contribution...Highly recommended.” —International Statistical Review |
| can causation be determined from an observational study: Causal Learning Alison Gopnik, Laura Schulz, 2007-03-22 Understanding causal structure is a central task of human cognition. Causal learning underpins the development of our concepts and categories, our intuitive theories, and our capacities for planning, imagination and inference. During the last few years, there has been an interdisciplinary revolution in our understanding of learning and reasoning: Researchers in philosophy, psychology, and computation have discovered new mechanisms for learning the causal structure of the world. This new work provides a rigorous, formal basis for theory theories of concepts and cognitive development, and moreover, the causal learning mechanisms it has uncovered go dramatically beyond the traditional mechanisms of both nativist theories, such as modularity theories, and empiricist ones, such as association or connectionism. |
| can causation be determined from an observational study: The Book of Why Judea Pearl, Dana Mackenzie, 2018-05-15 A Turing Award-winning computer scientist and statistician shows how understanding causality has revolutionized science and will revolutionize artificial intelligence Correlation is not causation. This mantra, chanted by scientists for more than a century, has led to a virtual prohibition on causal talk. Today, that taboo is dead. The causal revolution, instigated by Judea Pearl and his colleagues, has cut through a century of confusion and established causality -- the study of cause and effect -- on a firm scientific basis. His work explains how we can know easy things, like whether it was rain or a sprinkler that made a sidewalk wet; and how to answer hard questions, like whether a drug cured an illness. Pearl's work enables us to know not just whether one thing causes another: it lets us explore the world that is and the worlds that could have been. It shows us the essence of human thought and key to artificial intelligence. Anyone who wants to understand either needs The Book of Why. |
| can causation be determined from an observational 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) |
| can causation be determined from an observational study: Replication and Evidence Factors in Observational Studies Paul Rosenbaum, 2021-03-30 Outside of randomized experiments, association does not imply causation, and yet there is nothing defective about our knowledge that smoking causes lung cancer, a conclusion reached in the absence of randomized experimentation with humans. How is that possible? If observed associations do not identify causal effects in observational studies, how can a sequence of such associations become decisive? Two or more associations may each be susceptible to unmeasured biases, yet not susceptible to the same biases. An observational study has two evidence factors if it provides two comparisons susceptible to different biases that may be combined as if from independent studies of different data by different investigators, despite using the same data twice. If the two factors concur, then they may exhibit greater insensitivity to unmeasured biases than either factor exhibits on its own. Replication and Evidence Factors in Observational Studies includes four parts: A concise introduction to causal inference, making the book self-contained Practical examples of evidence factors from the health and social sciences with analyses in R The theory of evidence factors Study design with evidence factors A companion R package evident is available from CRAN. |
| can causation be determined from an observational study: Causation, Prediction, and Search Peter Spirtes, Clark Glymour, Richard Scheines, 2012-12-06 This book is intended for anyone, regardless of discipline, who is interested in the use of statistical methods to help obtain scientific explanations or to predict the outcomes of actions, experiments or policies. Much of G. Udny Yule's work illustrates a vision of statistics whose goal is to investigate when and how causal influences may be reliably inferred, and their comparative strengths estimated, from statistical samples. Yule's enterprise has been largely replaced by Ronald Fisher's conception, in which there is a fundamental cleavage between experimental and non experimental inquiry, and statistics is largely unable to aid in causal inference without randomized experimental trials. Every now and then members of the statistical community express misgivings about this turn of events, and, in our view, rightly so. Our work represents a return to something like Yule's conception of the enterprise of theoretical statistics and its potential practical benefits. If intellectual history in the 20th century had gone otherwise, there might have been a discipline to which our work belongs. As it happens, there is not. We develop material that belongs to statistics, to computer science, and to philosophy; the combination may not be entirely satisfactory for specialists in any of these subjects. We hope it is nonetheless satisfactory for its purpose. |
| can causation be determined from an observational study: Experimental and Quasi-experimental Designs for Generalized Causal Inference William R. Shadish, Thomas D. Cook, Donald Thomas Campbell, 2002 Sections include: experiments and generalised causal inference; statistical conclusion validity and internal validity; construct validity and external validity; quasi-experimental designs that either lack a control group or lack pretest observations on the outcome; quasi-experimental designs that use both control groups and pretests; quasi-experiments: interrupted time-series designs; regresssion discontinuity designs; randomised experiments: rationale, designs, and conditions conducive to doing them; practical problems 1: ethics, participation recruitment and random assignment; practical problems 2: treatment implementation and attrition; generalised causal inference: a grounded theory; generalised causal inference: methods for single studies; generalised causal inference: methods for multiple studies; a critical assessment of our assumptions. |
| can causation be determined from an observational study: Measuring Racial Discrimination National Research Council, Division of Behavioral and Social Sciences and Education, Committee on National Statistics, Panel on Methods for Assessing Discrimination, 2004-07-24 Many racial and ethnic groups in the United States, including blacks, Hispanics, Asians, American Indians, and others, have historically faced severe discriminationâ€pervasive and open denial of civil, social, political, educational, and economic opportunities. Today, large differences among racial and ethnic groups continue to exist in employment, income and wealth, housing, education, criminal justice, health, and other areas. While many factors may contribute to such differences, their size and extent suggest that various forms of discriminatory treatment persist in U.S. society and serve to undercut the achievement of equal opportunity. Measuring Racial Discrimination considers the definition of race and racial discrimination, reviews the existing techniques used to measure racial discrimination, and identifies new tools and areas for future research. The book conducts a thorough evaluation of current methodologies for a wide range of circumstances in which racial discrimination may occur, and makes recommendations on how to better assess the presence and effects of discrimination. |
| can causation be determined from an observational study: Causal Inference in Statistics Judea Pearl, Madelyn Glymour, Nicholas P. Jewell, 2016-01-25 CAUSAL INFERENCE IN STATISTICS A Primer Causality is central to the understanding and use of data. Without an understanding of cause–effect relationships, we cannot use data to answer questions as basic as Does this treatment harm or help patients? But though hundreds of introductory texts are available on statistical methods of data analysis, until now, no beginner-level book has been written about the exploding arsenal of methods that can tease causal information from data. Causal Inference in Statistics fills that gap. Using simple examples and plain language, the book lays out how to define causal parameters; the assumptions necessary to estimate causal parameters in a variety of situations; how to express those assumptions mathematically; whether those assumptions have testable implications; how to predict the effects of interventions; and how to reason counterfactually. These are the foundational tools that any student of statistics needs to acquire in order to use statistical methods to answer causal questions of interest. This book is accessible to anyone with an interest in interpreting data, from undergraduates, professors, researchers, or to the interested layperson. Examples are drawn from a wide variety of fields, including medicine, public policy, and law; a brief introduction to probability and statistics is provided for the uninitiated; and each chapter comes with study questions to reinforce the readers understanding. |
| can causation be determined from an observational study: The SAGE Encyclopedia of Communication Research Methods Mike Allen, 2017-04-11 Communication research is evolving and changing in a world of online journals, open-access, and new ways of obtaining data and conducting experiments via the Internet. Although there are generic encyclopedias describing basic social science research methodologies in general, until now there has been no comprehensive A-to-Z reference work exploring methods specific to communication and media studies. Our entries, authored by key figures in the field, focus on special considerations when applied specifically to communication research, accompanied by engaging examples from the literature of communication, journalism, and media studies. Entries cover every step of the research process, from the creative development of research topics and questions to literature reviews, selection of best methods (whether quantitative, qualitative, or mixed) for analyzing research results and publishing research findings, whether in traditional media or via new media outlets. In addition to expected entries covering the basics of theories and methods traditionally used in communication research, other entries discuss important trends influencing the future of that research, including contemporary practical issues students will face in communication professions, the influences of globalization on research, use of new recording technologies in fieldwork, and the challenges and opportunities related to studying online multi-media environments. Email, texting, cellphone video, and blogging are shown not only as topics of research but also as means of collecting and analyzing data. Still other entries delve into considerations of accountability, copyright, confidentiality, data ownership and security, privacy, and other aspects of conducting an ethical research program. Features: 652 signed entries are contained in an authoritative work spanning four volumes available in choice of electronic or print formats. Although organized A-to-Z, front matter includes a Reader’s Guide grouping entries thematically to help students interested in a specific aspect of communication research to more easily locate directly related entries. Back matter includes a Chronology of the development of the field of communication research; a Resource Guide to classic books, journals, and associations; a Glossary introducing the terminology of the field; and a detailed Index. Entries conclude with References/Further Readings and Cross-References to related entries to guide students further in their research journeys. The Index, Reader’s Guide themes, and Cross-References combine to provide robust search-and-browse in the e-version. |
| can causation be determined from an observational study: The Effect Nick Huntington-Klein, 2021-12-20 Extensive code examples in R, Stata, and Python Chapters on overlooked topics in econometrics classes: heterogeneous treatment effects, simulation and power analysis, new cutting-edge methods, and uncomfortable ignored assumptions An easy-to-read conversational tone Up-to-date coverage of methods with fast-moving literatures like difference-in-differences |
| can causation be determined from an observational study: Brocklehurst's Textbook of Geriatric Medicine and Gerontology E-Book Howard M. Fillit, Kenneth Rockwood, John B Young, 2016-05-06 The leading reference in the field of geriatric care, Brocklehurst's Textbook of Geriatric Medicine and Gerontology, 8th Edition, provides a contemporary, global perspective on topics of importance to today's gerontologists, internal medicine physicians, and family doctors. An increased focus on frailty, along with coverage of key issues in gerontology, disease-specific geriatrics, and complex syndromes specific to the elderly, makes this 8th Edition the reference you'll turn to in order to meet the unique challenges posed by this growing patient population. - Consistent discussions of clinical manifestations, diagnosis, prevention, treatment, and more make reference quick and easy. - More than 250 figures, including algorithms, photographs, and tables, complement the text and help you find what you need on a given condition. - Clinical relevance of the latest scientific findings helps you easily apply the material to everyday practice. - A new chapter on frailty, plus an emphasis on frailty throughout the book, addresses the complex medical and social issues that affect care, and the specific knowledge and skills essential for meeting your patients' complex needs. - New content brings you up to date with information on gerontechnology, emergency and pre-hospital care, HIV and aging, intensive treatment of older adults, telemedicine, the built environment, and transcultural geriatrics. - New editor Professor John Young brings a fresh perspective and unique expertise to this edition. |
| can causation be determined from an observational study: Experimental Political Science and the Study of Causality Rebecca B. Morton, Kenneth C. Williams, 2010-08-06 Increasingly, political scientists use the term 'experiment' or 'experimental' to describe their empirical research. One of the primary reasons for doing so is the advantage of experiments in establishing causal inferences. In this book, Rebecca B. Morton and Kenneth C. Williams discuss in detail how experiments and experimental reasoning with observational data can help researchers determine causality. They explore how control and random assignment mechanisms work, examining both the Rubin causal model and the formal theory approaches to causality. They also cover general topics in experimentation such as the history of experimentation in political science; internal and external validity of experimental research; types of experiments - field, laboratory, virtual, and survey - and how to choose, recruit, and motivate subjects in experiments. They investigate ethical issues in experimentation, the process of securing approval from institutional review boards for human subject research, and the use of deception in experimentation. |
| can causation be determined from an observational study: Concepts of Epidemiology Raj S. Bhopal, 2016 First edition published in 2002. Second edition published in 2008. |
| can causation be determined from an observational study: Quantitative Social Science Kosuke Imai, Lori D. Bougher, 2021-03-16 Princeton University Press published Imai's textbook, Quantitative Social Science: An Introduction, an introduction to quantitative methods and data science for upper level undergrads and graduates in professional programs, in February 2017. What is distinct about the book is how it leads students through a series of applied examples of statistical methods, drawing on real examples from social science research. The original book was prepared with the statistical software R, which is freely available online and has gained in popularity in recent years. But many existing courses in statistics and data sciences, particularly in some subject areas like sociology and law, use STATA, another general purpose package that has been the market leader since the 1980s. We've had several requests for STATA versions of the text as many programs use it by default. This is a translation of the original text, keeping all the current pedagogical text but inserting the necessary code and outputs from STATA in their place-- |
| can causation be determined from an observational study: Design of Observational Studies Paul R. Rosenbaum, 2009-10-22 An observational study is an empiric investigation of effects caused by treatments when randomized experimentation is unethical or infeasible. Observational studies are common in most fields that study the effects of treatments on people, including medicine, economics, epidemiology, education, psychology, political science and sociology. The quality and strength of evidence provided by an observational study is determined largely by its design. Design of Observational Studies is both an introduction to statistical inference in observational studies and a detailed discussion of the principles that guide the design of observational studies. Design of Observational Studies is divided into four parts. Chapters 2, 3, and 5 of Part I cover concisely, in about one hundred pages, many of the ideas discussed in Rosenbaum’s Observational Studies (also published by Springer) but in a less technical fashion. Part II discusses the practical aspects of using propensity scores and other tools to create a matched comparison that balances many covariates. Part II includes a chapter on matching in R. In Part III, the concept of design sensitivity is used to appraise the relative ability of competing designs to distinguish treatment effects from biases due to unmeasured covariates. Part IV discusses planning the analysis of an observational study, with particular reference to Sir Ronald Fisher’s striking advice for observational studies, make your theories elaborate. The second edition of his book, Observational Studies, was published by Springer in 2002. |
| can causation be determined from an observational study: Overcoming Multiple Sclerosis George Jelinek, 2016-07-01 Overcoming Multiple Sclerosis is an established and successful program of treatment. Once a diagnosis of MS meant inevitable decline and disability. Now thousands of people around the world are living healthy, active lives on the Overcoming Multiple Sclerosis recovery program. Overcoming Multiple Sclerosis explains the nature of MS and outlines an evidence-based 7 step program for recovery. Professor George Jelinek devised the program from an exhaustive analysis of medical research when he was first diagnosed with MS in 1999. It has been refined through major ongoing international clinical studies under Professor Jelinek's leadership, examining the lifestyles of several thousand people with MS world-wide and their health outcomes. Overcoming Multiple Sclerosis is invaluable for anyone recently diagnosed with MS, living with MS for years, or with a family member with MS. It makes an ideal resource for doctors treating people with MS. 'I would have no hesitation in recommending Overcoming Multiple Sclerosis to my patients, but also to my friends and colleagues.' Professor Gavin Giovannoni, MBBCh, PhD, FCP (S.A., Neurol.), FRCP, FRCPath, Chair of Neurology, Blizard Institute, Barts and The London School of Medicine and Dentistry 'Overcoming Multiple Sclerosis combines hard scientific evidence with practical advice and compassion. It will be of benefit to nearly everybody affected by MS and I heartily recommend it.' Dr Peter Fisher FRCP , Physician to Her Majesty Queen Elizabeth II, and Director of Research, Royal London Hospital for Integrated Medicine |
| can causation be determined from an observational study: Causal Inference in Statistics, Social, and Biomedical Sciences Guido W. Imbens, Donald B. Rubin, 2015-04-06 This text presents statistical methods for studying causal effects and discusses how readers can assess such effects in simple randomized experiments. |
| can causation be determined from an observational study: Handbook of Statistical Genomics David J. Balding, Ida Moltke, John Marioni, 2019-07-09 A timely update of a highly popular handbook on statistical genomics This new, two-volume edition of a classic text provides a thorough introduction to statistical genomics, a vital resource for advanced graduate students, early-career researchers and new entrants to the field. It introduces new and updated information on developments that have occurred since the 3rd edition. Widely regarded as the reference work in the field, it features new chapters focusing on statistical aspects of data generated by new sequencing technologies, including sequence-based functional assays. It expands on previous coverage of the many processes between genotype and phenotype, including gene expression and epigenetics, as well as metabolomics. It also examines population genetics and evolutionary models and inference, with new chapters on the multi-species coalescent, admixture and ancient DNA, as well as genetic association studies including causal analyses and variant interpretation. The Handbook of Statistical Genomics focuses on explaining the main ideas, analysis methods and algorithms, citing key recent and historic literature for further details and references. It also includes a glossary of terms, acronyms and abbreviations, and features extensive cross-referencing between chapters, tying the different areas together. With heavy use of up-to-date examples and references to web-based resources, this continues to be a must-have reference in a vital area of research. Provides much-needed, timely coverage of new developments in this expanding area of study Numerous, brand new chapters, for example covering bacterial genomics, microbiome and metagenomics Detailed coverage of application areas, with chapters on plant breeding, conservation and forensic genetics Extensive coverage of human genetic epidemiology, including ethical aspects Edited by one of the leading experts in the field along with rising stars as his co-editors Chapter authors are world-renowned experts in the field, and newly emerging leaders. The Handbook of Statistical Genomics is an excellent introductory text for advanced graduate students and early-career researchers involved in statistical genetics. |
| can causation be determined from an observational 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. |
| can causation be determined from an observational study: Principles and Practice of Clinical Research John I. Gallin, Frederick P Ognibene, 2011-04-28 The second edition of this innovative work again provides a unique perspective on the clinical discovery process by providing input from experts within the NIH on the principles and practice of clinical research. Molecular medicine, genomics, and proteomics have opened vast opportunities for translation of basic science observations to the bedside through clinical research. As an introductory reference it gives clinical investigators in all fields an awareness of the tools required to ensure research protocols are well designed and comply with the rigorous regulatory requirements necessary to maximize the safety of research subjects. Complete with sections on the history of clinical research and ethics, copious figures and charts, and sample documents it serves as an excellent companion text for any course on clinical research and as a must-have reference for seasoned researchers.*Incorporates new chapters on Managing Conflicts of Interest in Human Subjects Research, Clinical Research from the Patient's Perspective, The Clinical Researcher and the Media, Data Management in Clinical Research, Evaluation of a Protocol Budget, Clinical Research from the Industry Perspective, and Genetics in Clinical Research *Addresses the vast opportunities for translation of basic science observations to the bedside through clinical research*Delves into data management and addresses how to collect data and use it for discovery*Contains valuable, up-to-date information on how to obtain funding from the federal government |
| can causation be determined from an observational study: Causal Inference Scott Cunningham, 2021-01-26 An accessible, contemporary introduction to the methods for determining cause and effect in the Social Sciences “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages. |
| can causation be determined from an observational study: Mendelian Randomization Stephen Burgess, Simon G. Thompson, 2015-03-06 Presents the Terminology and Methods of Mendelian Randomization for Epidemiological StudiesMendelian randomization uses genetic instrumental variables to make inferences about causal effects based on observational data. It, therefore, can be a reliable way of assessing the causal nature of risk factors, such as biomarkers, for a wide range of disea |
| can causation be determined from an observational study: Analysis of Observational Health Care Data Using SAS Douglas E. Faries, Andrew C. Leon, Josep Maria Haro, Robert L. Obenchain, 2010 This book guides researchers in performing and presenting high-quality analyses of all kinds of non-randomized studies, including analyses of observational studies, claims database analyses, assessment of registry data, survey data, pharmaco-economic data, and many more applications. The text is sufficiently detailed to provide not only general guidance, but to help the researcher through all of the standard issues that arise in such analyses. Just enough theory is included to allow the reader to understand the pros and cons of alternative approaches and when to use each method. The numerous contributors to this book illustrate, via real-world numerical examples and SAS code, appropriate implementations of alternative methods. The end result is that researchers will learn how to present high-quality and transparent analyses that will lead to fair and objective decisions from observational data. This book is part of the SAS Press program. |
| can causation be determined from an observational study: The Politics of Resentment Katherine J. Cramer, 2016-03-23 “An important contribution to the literature on contemporary American politics. Both methodologically and substantively, it breaks new ground.” —Journal of Sociology & Social Welfare When Scott Walker was elected Governor of Wisconsin, the state became the focus of debate about the appropriate role of government. In a time of rising inequality, Walker not only survived a bitterly contested recall, he was subsequently reelected. But why were the very people who would benefit from strong government services so vehemently against the idea of big government? With The Politics of Resentment, Katherine J. Cramer uncovers an oft-overlooked piece of the puzzle: rural political consciousness and the resentment of the “liberal elite.” Rural voters are distrustful that politicians will respect the distinct values of their communities and allocate a fair share of resources. What can look like disagreements about basic political principles are therefore actually rooted in something even more fundamental: who we are as people and how closely a candidate’s social identity matches our own. Taking a deep dive into Wisconsin’s political climate, Cramer illuminates the contours of rural consciousness, showing how place-based identities profoundly influence how people understand politics. The Politics of Resentment shows that rural resentment—no less than partisanship, race, or class—plays a major role in dividing America against itself. |
| can causation be determined from an observational study: 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. |
| can causation be determined from an observational study: Hunter's Diseases of Occupations Peter Baxter, Tar-Ching Aw, Anne Cockcroft, Paul Durrington, J Malcolm Harrington, 2010-10-29 Winner of the 2011 BMA book awards: medicine categoryIn the five decades since its first publication, Hunter's Diseases of Occupations has remained the pre-eminent text on diseases caused by work, universally recognized as the most authoritative source of information in the field. It is an important guide for doctors in all disciplines who may |
| can causation be determined from an observational study: Secondary Analysis of Electronic Health Records MIT Critical Data, 2016-09-09 This book trains the next generation of scientists representing different disciplines to leverage the data generated during routine patient care. It formulates a more complete lexicon of evidence-based recommendations and support shared, ethical decision making by doctors with their patients. Diagnostic and therapeutic technologies continue to evolve rapidly, and both individual practitioners and clinical teams face increasingly complex ethical decisions. Unfortunately, the current state of medical knowledge does not provide the guidance to make the majority of clinical decisions on the basis of evidence. The present research infrastructure is inefficient and frequently produces unreliable results that cannot be replicated. Even randomized controlled trials (RCTs), the traditional gold standards of the research reliability hierarchy, are not without limitations. They can be costly, labor intensive, and slow, and can return results that are seldom generalizable to every patient population. Furthermore, many pertinent but unresolved clinical and medical systems issues do not seem to have attracted the interest of the research enterprise, which has come to focus instead on cellular and molecular investigations and single-agent (e.g., a drug or device) effects. For clinicians, the end result is a bit of a “data desert” when it comes to making decisions. The new research infrastructure proposed in this book will help the medical profession to make ethically sound and well informed decisions for their patients. |
| can causation be determined from an observational 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. |
| can causation be determined from an observational study: Experiments in Public Management Research Oliver James, Sebastian R. Jilke, Gregg G. Van Ryzin, 2017-07-27 An overview of experimental research and methods in public management, and their impact on theory, research practices and substantive knowledge. |
| can causation be determined from an observational study: Handbook of EHealth Evaluation Francis Yin Yee Lau, Craig Kuziemsky, 2016-11 To order please visit https://onlineacademiccommunity.uvic.ca/press/books/ordering/ |
| can causation be determined from an observational study: An Introduction to Causal Inference Judea Pearl, 2015 This paper summarizes recent advances in causal inference and underscores the paradigmatic shifts that must be undertaken in moving from traditional statistical analysis to causal analysis of multivariate data. Special emphasis is placed on the assumptions that underly all causal inferences, the languages used in formulating those assumptions, the conditional nature of all causal and counterfactual claims, and the methods that have been developed for the assessment of such claims. These advances are illustrated using a general theory of causation based on the Structural Causal Model (SCM) described in Pearl (2000a), which subsumes and unifies other approaches to causation, and provides a coherent mathematical foundation for the analysis of causes and counterfactuals. In particular, the paper surveys the development of mathematical tools for inferring (from a combination of data and assumptions) answers to three types of causal queries: (1) queries about the effects of potential interventions, (also called causal effects or policy evaluation) (2) queries about probabilities of counterfactuals, (including assessment of regret, attribution or causes of effects) and (3) queries about direct and indirect effects (also known as mediation). Finally, the paper defines the formal and conceptual relationships between the structural and potential-outcome frameworks and presents tools for a symbiotic analysis that uses the strong features of both. The tools are demonstrated in the analyses of mediation, causes of effects, and probabilities of causation. -- p. 1. |
| can causation be determined from an observational study: Spurious Correlations Tyler Vigen, 2015-05-12 Spurious Correlations ... is the most fun you'll ever have with graphs. -- Bustle Military intelligence analyst and Harvard Law student Tyler Vigen illustrates the golden rule that correlation does not equal causation through hilarious graphs inspired by his viral website. Is there a correlation between Nic Cage films and swimming pool accidents? What about beef consumption and people getting struck by lightning? Absolutely not. But that hasn't stopped millions of people from going to tylervigen.com and asking, Wait, what? Vigen has designed software that scours enormous data sets to find unlikely statistical correlations. He began pulling the funniest ones for his website and has since gained millions of views, hundreds of thousands of likes, and tons of media coverage. Subversive and clever, Spurious Correlations is geek humor at its finest, nailing our obsession with data and conspiracy theory. |
| can causation be determined from an observational study: Explanation in Causal Inference Tyler J. VanderWeele, 2015 A comprehensive examination of methods for mediation and interaction, VanderWeele's book is the first to approach this topic from the perspective of causal inference. Numerous software tools are provided, and the text is both accessible and easy to read, with examples drawn from diverse fields. The result is an essential reference for anyone conducting empirical research in the biomedical or social sciences. |
| can causation be determined from an observational study: Data Analysis Using Regression and Multilevel/Hierarchical Models Andrew Gelman, Jennifer Hill, 2007 This book, first published in 2007, is for the applied researcher performing data analysis using linear and nonlinear regression and multilevel models. |
| can causation be determined from an observational study: Handbook of Causal Analysis for Social Research Stephen L. Morgan, 2013-04-22 What constitutes a causal explanation, and must an explanation be causal? What warrants a causal inference, as opposed to a descriptive regularity? What techniques are available to detect when causal effects are present, and when can these techniques be used to identify the relative importance of these effects? What complications do the interactions of individuals create for these techniques? When can mixed methods of analysis be used to deepen causal accounts? Must causal claims include generative mechanisms, and how effective are empirical methods designed to discover them? The Handbook of Causal Analysis for Social Research tackles these questions with nineteen chapters from leading scholars in sociology, statistics, public health, computer science, and human development. |
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1 Experiments and Observational Studies - math.colgate.edu
In an observational study, the subjects choose, or are naturally in, either the treatment group or control group. While an observational study can show an association, like smoking is asso …
determining causation from Observational Studies: a …
tions. Richards’ study provides strong evidence of causation, as did their later study on genetically determined obesity and MS risk (11), and backs up prospective observational studies such as …
Study Design VI - Ecological Studies - Nature
An ecological study is an observational study defined by the level at which data are analysed, namely at the population or group ... causation, the population context of indi-
Causal Inference: A Tutorial - Duke University
Questions on Causation I Relevant questions about causation: I the philosophical meaningfulness of the notion of causation I deducing the causes of a given effect I understanding the details of …
Sensitivity Analysis in Observational Research: Introducing …
with observational data is bias by unmeasured or uncontrolled confounding, i.e., that some third factor related to both the treatment and the outcome might explain their association, with no …
The Relationship of Ready-to-Eat Cereal Intake and Body …
Assessment of study quality Risk of bias for each trial was assessed with the Cochrane Risk-of-Bias tool (Cochrane, version 2) for randomized trials (all trials were randomly assigned), using …
An Example Of An Observational Study Copy
An Example Of An Observational Study An Example of an Observational Study: A Comprehensive Guide Observational studies are a crucial research method in various fields, …
Special issue: Responsible writing in science Lessons in …
Observational study designs, also called epidemiologic study designs, are often retrospective and are used to assess potential causation in expo- sure-outcome relationships and therefore …
Johns Hopkins Evidence-Based Practice Model and Guideline …
study design A type of observational study that analyzes data from a population at a specific point in time. Cross-sectional study designs typically collect data with surveys, observations, and …
Causal Inference in Geoscience and Remote Sensing from …
In this paper, we focus on observational causal inference, thus we try to estimate the correct direction of causation using a finite set of empirical data. In addition, we focus on the more …
A. Numerical variable - Kent
Researchers conducted a study and determined that students who carpool have less friends than students who ride the bus to school. Can we conclude that carpooling causes students to have …
CHAPTER 15. INTRODUCTION TO DESIGN - Statistics
What to Do if We Have Observational Studies If you can, do a controlled (i.e. randomized) study. If you have to do with an observational study, then you can and should try to “control for …
Selecting the appropriate study design: Case–control and …
This article discusses the observational analytic study designs, i.e., case–control and cohort studies. These two study ... (controls) groups. The odds ratio is determined to compare the …
causation, only association. Observational studies do not …
observational study, the researcher observes the behavior of the individuals in the study without trying to influence the outcome of the study. In research, we wish to determine how varying the …
What are observational studies and how do they differ from …
Traditional cohort Observational database Study visits At regular defined intervals As and when patient attends for care Data entry Often ... Loss to follow-up May be substantial, but can be …
Study Designs in Epidemiology - pre-med.jumedicine.com
exposed to a risk factor (study group) is compared with a group of individuals not exposed to the risk factor control group….and all followed up to monitor occurrence of disease. Cohort study is …
Lecture 6/Chapters 5&6 Observational Studies & Review
Retrospective observational study: researchers record variables’ values backward in time, about the past. Prospective observational study: researchers record variables’ values forward in time …
Introduction to Causal Directed Acyclic Graphs - Stanford …
Jan 28, 2019 · • Observational comparative effectiveness . 1 • Treatments not assigned, determined by mechanisms of routine practice • Actual mechanisms are often unknown • …
Statistical models for causation: what inferential leverage do …
models for causation, as applied to experimental and observational data. The intention-to-treat principle and the effect of treatment on the treated will also be discussed. Flaws in per-protocol …
Effect modification, interaction and mediation: an overview of ...
on observational data, the same principle from the RCT can be applied. For example, the Million Women Study was a cohort study including about one of every four women aged 50–64 years …
Study Design in Causal Models - JSTOR
can be determined by the researcher. Usually, causal variables determined by the researcher are known, but in principle, they can be also unknown if the information on the values set for the …
introduction to observational studies - UPEI Projects
can be tested. In many instances the study subjects will be exposed to the risk factor(s) whether the study is done or not, and thus, observational studies can capitalise on these ‘natural …
Causal Analyses Using Structural Equation Models
Dec 7, 2008 · a cause under study to its effect of interest. Alternatively, ‘Which variables to control in the model to control for confounding?’. A back-door path can convey a spurious relationship …
coursera Chapter 1
Experiment vsObservational Study An observational studyis a study in which the researcher does not actively control the value of any variable, but simply observes the values as they naturally …
1 Experiments and Observational Studies - Colgate
In an observational study, the subjects choose, or are naturally in, either the treatment group or control group. While an observational study can show an association, like smoking is asso …
Research Methodology Series - Indian Pediatrics
An analytical study tests a hypothesis to determine an association between two or more variables, like causation, risk, or effect. Such studies have two or more study groups for comparison. The …
Observational Studies: Uses 31 and Limitations - Springer
from study participants [Bias1, or unmeasur]. - able distortions in the characteristics of selected patients compared to the theoretical study popu-lation, is a major threat to the validity of all …
Do We Necessarily Need Longitudinal Data to Infer Causal …
Longitudinal data can either be prospective or retrospective. In a prospective study, a panel study or a disease register, individuals are followed over time, and data on putative causes and their …
Incidence of cavity problems after open cavity mastoidectomy …
involved in the causation of cavity problems. Methods: This prospective observational study conducted in the Department of ENT, Nalanda Medical College and Hospital, Patna, Bihar, …
Systematic reviews to evaluate causation: an overview of …
Assessing study design and quality Epidemiological principles relevant to studying causation without bias include prospective design; measurement of causal agent, correct temporality of …
Causal Language To use or not to use…. - Stanford Medicine
studies assessing independent and dependent variables, unless the study design is a diagnostic/prognostic study andhas used an appropriate approach to quantifying predictive …
Observational Study Design Part I - osctr.ouhsc.edu
observational nature of the study, we can comment on associations, but not causation. Another drawback is that with the longitudinal design, loss to follow‐up is likely and can lead to biased …
Randomize d Controlled & Observational - campus.sanofi
of causation helps support decision making 1,2 Both randomized controlled trials and observational studies can be used to generate medical evidence 2 Randomiation can provide a …
Does a dose–response relationship reduce sensitivity to …
may or may not reduce sensitivity to hidden bias, and whether it has or has not can be determined by a suitable analysis using the data at hand. Moreover, a study without a dose–response …
Correlation and Causation in the Study of Personality
Correlation and Causation in the Study of Personality JAMES J. LEE* Department of Psychology, Harvard University, Cambridge, MA, USA ... Because of the observational nature of the …
Experimental Studies and Observational Studies - Springer
design to study their research questions. At a general level, observational (non-experimental) studies and experimental studies can be distin-guished (Fig. 1). The fundamental difference …
Ways of Learning: Observational Studies Versus …
observational study can be analyzed similarly to an experiment, one is less certain that the presumed treatment actually caused the observed response. Because the investigator does …
Cohort studies investigating the effects of exposures: key …
The differentiating characteristics between observational (e.g., cohort study) and experimental (e.g., RCT) study designs are that in the former the investigator does not intervene and rather
Using Statistics to Determine Causal Relationships - Duke …
First, we establish some terminology that describes the basics of a causal study. Treatments are variables that are conceptually manipulable. For example, in a study addressing ways of …
Determining Causation from Observational Studies: A …
determining causation from Observational Studies: a challenge for Modern neuroepidemiology George A. Jelinek* ... While epidemiology is the study of frequencies, trends, and determinants …
Artificial Intelligence can spot when correlation really does …
causation 6/2/2020 Peer-reviewed Modelling & Observational Study Medical data • AI can merge overlapping and incomplete medical datasets and then determine which variables are …
Can Observational Studies Show Causation
Can Observational Studies Show Causation Alison Gopnik,Laura Schulz Critical Appraisal of Epidemiological Studies and Clinical Trials Mark Elwood,2007-02-22 This book presents a …
Class 9: Experiments and Observational Studies (Text: Section …
A statistical study can tell us • What is going on (the characteristics of a population) • Effect of an intervention (taking a drug, using a new fertilizer, implementing a policy to create ... Even if the …
Statistical Models for Causation: What Inferential Leverage …
Observational studies will be considered, with procedures for handling confounders by stratification or by making statistical adjustments. How-ever, the starting point is experiments. …
Causal Effects in Observational Studies - stat20.org
Natural experiment A study in which researchers did not randomly assign treatment but claim that the treatment process is sufficiently independent of covariates to justify treat-ment effect …
Association & Causation in epidemiological studies - JU …
A researcher in his observational study found that the average serum homocysteine among patients of IHD was 15 mcg/dl (Normal=10-12 mcg/dl)! Can we say that Hyperhomocystenemia …
Relationships Between Two Variables: Regression and …
When can We Conclude Causation from an Observational Study? When all of the following are true: • When the association is strong and consistent. • When extreme values of the “cause …
introduction to observational studies - UPEI Projects
3. Describe the general strength and weaknesses of experimental versus observational study designs for the identification and evaluation of causal factors. 4. Describe the three main …
Causal Effects in Observational Studies - Stat 20: Introduction …
Natural experiment A study in which researchers did not randomly assign treatment but claim that the treatment process is sufficiently independent of covariates to justify treat-ment effect …
