STROBE Initiative: Guidelines on Reporting Observational Studies
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A Catalogue of Reporting Guidelines for Health Research REVIEW
DOI: 10.1111/j.1365-2362.2009.02234.x REVIEW A catalogue of reporting guidelines for health research I. Simera*, D. Moher†, J. Hoey‡, K. F. Schulz§ and D. G. Altman* *University of Oxford, Oxford, UK, †Ottawa Health Research Institute, Ottawa, ON, Canada, ‡University of Toronto, Toronto, ON, Canada, §UNC School of Medicine, Chapel Hill, NC, USA ABSTRACT Growing evidence demonstrates widespread deficiencies in the reporting of health research studies. The EQUATOR Network is an international initiative that aims to enhance the reliability and value of the published health research literature. EQUATOR provides resources, education and training to facilitate good research reporting and assists in the development, dissemination and implementation of robust reporting guidelines. This paper presents a collection of tools and guidelines available on the EQUATOR website (http://www.equator- network.org) that have been developed to increase the accuracy and transparency of health research reporting. Keywords EQUATOR Network, reporting guidelines, research reporting. Eur J Clin Invest 2010; 40 (1): 35–53 Many scientific articles are written merely to get something reporting of harms [7]; presenting data and graphs in confusing published, neglecting the clinician who would like the medical and misleading ways [8]; and omissions from or misinterpreta- literature to guide their practice [1]. tion of results in abstracts [9]. These deficiencies seriously distort scientific reality [10] and Introduction prevent clinicians from applying effective interventions in patients’ care [5]. Boxes 1 and 2 show just two of the numerous The main purpose of investing money into health research is to examples of problems identified in the research literature. -
Observational Clinical Research
E REVIEW ARTICLE Clinical Research Methodology 2: Observational Clinical Research Daniel I. Sessler, MD, and Peter B. Imrey, PhD * † Case-control and cohort studies are invaluable research tools and provide the strongest fea- sible research designs for addressing some questions. Case-control studies usually involve retrospective data collection. Cohort studies can involve retrospective, ambidirectional, or prospective data collection. Observational studies are subject to errors attributable to selec- tion bias, confounding, measurement bias, and reverse causation—in addition to errors of chance. Confounding can be statistically controlled to the extent that potential factors are known and accurately measured, but, in practice, bias and unknown confounders usually remain additional potential sources of error, often of unknown magnitude and clinical impact. Causality—the most clinically useful relation between exposure and outcome—can rarely be defnitively determined from observational studies because intentional, controlled manipu- lations of exposures are not involved. In this article, we review several types of observa- tional clinical research: case series, comparative case-control and cohort studies, and hybrid designs in which case-control analyses are performed on selected members of cohorts. We also discuss the analytic issues that arise when groups to be compared in an observational study, such as patients receiving different therapies, are not comparable in other respects. (Anesth Analg 2015;121:1043–51) bservational clinical studies are attractive because Group, and the American Society of Anesthesiologists they are relatively inexpensive and, perhaps more Anesthesia Quality Institute. importantly, can be performed quickly if the required Recent retrospective perioperative studies include data O 1,2 data are already available. -
Guidelines for Reporting Health Research: How to Promote Their Use in Your Journal
Guidelines for reporting health research: How to promote their use in your journal Written by the EQUATOR Network Group; updated March 2015 Key points: Reporting guidelines help to improve the accuracy, transparency and completeness of health research publications and increase the value of published research EQUATOR provides free online resources in English at www.equator-network.org and in Spanish at www.espanol.equator-network.org including a comprehensive collection of reporting guidelines for health research studies EQUATOR recommends that editors: explore the available reporting guidelines; select well developed guidelines appropriate for the reporting of research studies published by their journal; ask authors to adhere to these guidelines and ask peer reviewers to use them when assessing manuscripts In this document you will find information on: How your journal can support better reporting of health research . How to introduce reporting guidelines into your journal . How to select reporting guidelines for your journal . How and where to use reporting guidelines in a journal How to refer to the EQUATOR Network and reporting guidelines in your Instructions to Authors and Instructions for Peer Reviewers How to describe the EQUATOR Network; text that might be inserted into your Instructions to Authors; examples of how some journals encourage the use of reporting guidelines and refer to EQUATOR Introduction Although the ultimate responsibility for the design, conduct and accurate publication of research studies lies with the researchers, editors “should take all reasonable steps to ensure the quality of the material they publish”(1). Guidelines for reporting health research are important tools to facilitate this task. -
Participant Manual Research Transparency and Reproducibility Training (RT2) April 4-6, 2018 Amsterdam, Netherlands Contents
Participant Manual Research Transparency and Reproducibility Training (RT2) April 4-6, 2018 Amsterdam, Netherlands Contents About RT2 ..................................................................................................................................................................................................................... 2 Pre-Training Actions .......................................................................................................................................................................................... 3 1. Establish an OSF Account ..................................................................................................................................................... 3 2. Review study pre-registration ........................................................................................................................................... 3 3. Install Git and Create Github.com Account .......................................................................................................... 3 4. Install software for Dynamic Documents .............................................................................................................. 3 5. Install LaTeX ....................................................................................................................................................................................... 4 6. Install a Decent Text Editor ................................................................................................................................................. -
Observational Studies and Bias in Epidemiology
The Young Epidemiology Scholars Program (YES) is supported by The Robert Wood Johnson Foundation and administered by the College Board. Observational Studies and Bias in Epidemiology Manuel Bayona Department of Epidemiology School of Public Health University of North Texas Fort Worth, Texas and Chris Olsen Mathematics Department George Washington High School Cedar Rapids, Iowa Observational Studies and Bias in Epidemiology Contents Lesson Plan . 3 The Logic of Inference in Science . 8 The Logic of Observational Studies and the Problem of Bias . 15 Characteristics of the Relative Risk When Random Sampling . and Not . 19 Types of Bias . 20 Selection Bias . 21 Information Bias . 23 Conclusion . 24 Take-Home, Open-Book Quiz (Student Version) . 25 Take-Home, Open-Book Quiz (Teacher’s Answer Key) . 27 In-Class Exercise (Student Version) . 30 In-Class Exercise (Teacher’s Answer Key) . 32 Bias in Epidemiologic Research (Examination) (Student Version) . 33 Bias in Epidemiologic Research (Examination with Answers) (Teacher’s Answer Key) . 35 Copyright © 2004 by College Entrance Examination Board. All rights reserved. College Board, SAT and the acorn logo are registered trademarks of the College Entrance Examination Board. Other products and services may be trademarks of their respective owners. Visit College Board on the Web: www.collegeboard.com. Copyright © 2004. All rights reserved. 2 Observational Studies and Bias in Epidemiology Lesson Plan TITLE: Observational Studies and Bias in Epidemiology SUBJECT AREA: Biology, mathematics, statistics, environmental and health sciences GOAL: To identify and appreciate the effects of bias in epidemiologic research OBJECTIVES: 1. Introduce students to the principles and methods for interpreting the results of epidemio- logic research and bias 2. -
Study Types Transcript
Study Types in Epidemiology Transcript Study Types in Epidemiology Welcome to “Study Types in Epidemiology.” My name is John Kobayashi. I’m on the Clinical Faculty at the Northwest Center for Public Health Practice, at the School of Public Health and Community Medicine, University of Washington in Seattle. From 1982 to 2001, I was the state epidemiologist for Communicable Diseases at the Washington State Department of Health. Since 2001, I’ve also been the foreign adviser for the Field Epidemiology Training Program of Japan. About this Module The modules in the epidemiology series from the Northwest Center for Public Health Practice are intended for people working in the field of public health who are not epidemiologists, but who would like to increase their understanding of the epidemiologic approach to health and disease. This module focuses on descriptive and analytic epide- miology and their respective study designs. Before you go on with this module, we recommend that you become familiar with the following modules, which you can find on the Center’s Web site: What is Epidemiology in Public Health? and Data Interpretation for Public Health Professionals. We introduce a number of new terms in this module. If you want to review their definitions, the glossary in the attachments link at the top of the screen may be useful. Objectives By now, you should be familiar with the overall approach of epidemiology, the use of various kinds of rates to measure disease frequency, and the various ways in which epidemiologic data can be presented. This module offers an overview of descriptive and analytic epidemiology and the types of studies used to review and investigate disease occurrence and causes. -
Chapter 5 Experiments, Good And
Chapter 5 Experiments, Good and Bad Point of both observational studies and designed experiments is to identify variable or set of variables, called explanatory variables, which are thought to predict outcome or response variable. Confounding between explanatory variables occurs when two or more explanatory variables are not separated and so it is not clear how much each explanatory variable contributes in prediction of response variable. Lurking variable is explanatory variable not considered in study but confounded with one or more explanatory variables in study. Confounding with lurking variables effectively reduced in randomized comparative experiments where subjects are assigned to treatments at random. Confounding with a (only one at a time) lurking variable reduced in observational studies by controlling for it by comparing matched groups. Consequently, experiments much more effec- tive than observed studies at detecting which explanatory variables cause differences in response. In both cases, statistically significant observed differences in average responses implies differences are \real", did not occur by chance alone. Exercise 5.1 (Experiments, Good and Bad) 1. Randomized comparative experiment: effect of temperature on mice rate of oxy- gen consumption. For example, mice rate of oxygen consumption 10.3 mL/sec when subjected to 10o F. temperature (Fo) 0 10 20 30 ROC (mL/sec) 9.7 10.3 11.2 14.0 (a) Explanatory variable considered in study is (choose one) i. temperature ii. rate of oxygen consumption iii. mice iv. mouse weight 25 26 Chapter 5. Experiments, Good and Bad (ATTENDANCE 3) (b) Response is (choose one) i. temperature ii. rate of oxygen consumption iii. -
Analytic Epidemiology Analytic Studies: Observational Study
Analytic Studies: Observational Study Designs Cohort Studies Analytic Epidemiology Prospective Cohort Studies Retrospective (historical) Cohort Studies Part 2 Case-Control Studies Nested case-control Case-cohort studies Dr. H. Stockwell Case-crossover studies Determinants of disease: Analytic Epidemiology Epidemiology: Risk factors Identifying the causes of disease A behavior, environmental exposure, or inherent human characteristic that is associated with an important health Testing hypotheses using epidemiologic related condition* studies Risk factors are associated with an increased probability of disease but may Goal is to prevent disease (deterrents) not always cause the diseases *Last, J. Dictionary of Epidemiology Analytic Studies: Cohort Studies Panel Studies Healthy subjects are defined by their Combination of cross-sectional and exposure status and followed over time to cohort determine the incidence of disease, symptoms or death Same individuals surveyed at several poiiiints in time Subjects grouped by exposure level – exposed and unexposed (f(reference group, Can measure changes in individuals comparison group) Analytic Studies: Case-Control Studies Nested case-control studies A group of individuals with a disease Case-control study conducted within a (cases) are compared with a group of cohort study individuals without the disease (controls) Advantages of both study designs Also called a retrospective study because it starts with people with disease and looks backward for ppprevious exposures which might be -
1 Sensitivity Analysis in Observational Research: Introducing the E-‐Value
Sensitivity Analysis in Observational Research: Introducing the E-value Tyler J. VanderWeele, Ph.D., Departments of Epidemiology and Biostatistics, Harvard T.H. Chan School of Public Health Peng Ding, Ph.D., Department of Statistics, University of California, Berkeley This is the prepublication, author-produced version of a manuscript accepted for publication in Annals of Internal Medicine. This version does not include post-acceptance editing and formatting. The American College of Physicians, the publisher of Annals of Internal Medicine, is not responsible for the content or presentation of the author-produced accepted version of the manuscript or any version that a third party derives from it. Readers who wish to access the definitive published version of this manuscript and any ancillary material related to this manuscript (e.g., correspondence, corrections, editorials, linked articles) should go to Annals.org or to the print issue in which the article appears. Those who cite this manuscript should cite the published version, as it is the official version of record: VanderWeele, T.J. and Ding, P. (2017). Sensitivity analysis in observational research: introducing the E-value. Annals of Internal Medicine, 167(4):268-274. 1 Abstract. Sensitivity analysis can be useful in assessing how robust associations are to potential unmeasured or uncontrolled confounding. In this paper we introduce a new measure that we call the “E-value,” a measure related to the evidence for causality in observational studies, when they are potentially subject to confounding. The E-value is defined as the minimum strength of association on the risk ratio scale that an unmeasured confounder would need to have with both the treatment and the outcome to fully eXplain away a specific treatment-outcome association, conditional on the measured covariates. -
Chapter 20 Design and Analysis of Experiments and Observational
Chapter 20 Design and Analysis of Experiments and Observational Studies Point of both observational studies and designed experiments is to identify variable or set of variables, called explanatory variables, which are thought to predict outcome or response variable. Observational studies are an investigation of the association between treatment and response, where subject who decide whether or not they take a treatment. In experimental designs, experimenter decides who is given a treat- ment and who is to be a control, the experimenter alters levels of treatments when investigating how these alterations cause change in the response. 20.1 Observational Studies Exercise 20.1 (Observational Studies) 1. Observational Study versus Designed Experiment. (a) Effect of air temperature on rate of oxygen consumption (ROC) of four mice is investigated. ROC of one mouse at 0o F is 9.7 mL/sec for example. temperature (Fo) 0 10 20 30 ROC (mL/sec) 9.7 10.3 11.2 14.0 Since experimenter (not a mouse!) decides which mice are subjected to which temperature, this is (choose one) observational study / designed experiment. (b) Indiana police records from 1999–2001 on six drivers are analyzed to de- termine if there is an association between drinking and traffic accidents. One heavy drinker had 6 accidents for example. 169 170Chapter 20. Design and Analysis of Experiments and Observational Studies (lecture notes 10) drinking → heavy light 3 1 6 2 2 1 This is an observational study because (choose one) i. police decided who was going to drink and drive and who was not. ii. drivers decided who was going to drink and drive and who was not. -
Meta-Analysis of Observational Studies in Epidemiology (MOOSE)
CONSENSUS STATEMENT Meta-analysis of Observational Studies in Epidemiology A Proposal for Reporting Donna F. Stroup, PhD, MSc Objective Because of the pressure for timely, informed decisions in public health and Jesse A. Berlin, ScD clinical practice and the explosion of information in the scientific literature, research Sally C. Morton, PhD results must be synthesized. Meta-analyses are increasingly used to address this prob- lem, and they often evaluate observational studies. A workshop was held in Atlanta, Ingram Olkin, PhD Ga, in April 1997, to examine the reporting of meta-analyses of observational studies G. David Williamson, PhD and to make recommendations to aid authors, reviewers, editors, and readers. Drummond Rennie, MD Participants Twenty-seven participants were selected by a steering committee, based on expertise in clinical practice, trials, statistics, epidemiology, social sciences, and biomedi- David Moher, MSc cal editing. Deliberations of the workshop were open to other interested scientists. Fund- Betsy J. Becker, PhD ing for this activity was provided by the Centers for Disease Control and Prevention. Theresa Ann Sipe, PhD Evidence We conducted a systematic review of the published literature on the con- duct and reporting of meta-analyses in observational studies using MEDLINE, Educa- Stephen B. Thacker, MD, MSc tional Research Information Center (ERIC), PsycLIT, and the Current Index to Statistics. for the Meta-analysis Of We also examined reference lists of the 32 studies retrieved and contacted experts in Observational Studies in the field. Participants were assigned to small-group discussions on the subjects of bias, Epidemiology (MOOSE) Group searching and abstracting, heterogeneity, study categorization, and statistical methods. -
Statistical Overview for Clinical Trials Basics of Design and Analysis of Controlled Clinical Trials
Statistical Overview for Clinical Trials Basics of Design and Analysis of Controlled Clinical Trials Presented by: Behrang Vali M.S., CDER/OTS/OB/DB3 Special Thanks to: LaRee Tracy, Mike Welch, Ruthanna Davi, and Janice Derr 1 Disclaimer The views expressed in this presentation do not necessarily represent those of the U.S. Food and Drug Administration. 2 Common Study Designs • Case-Control Study – Retrospective Observational Study • Cohort Study – Prospective Observational Study • e.g. Natural History Studies • Controlled Clinical Trial (focus of this portion of the presentation) – Prospective Experimental Study – Best design to study causality – Utilized for design of adequate and well- controlled trials 3 Study Objective/Research Question Study Population of Interest and Design Endpoint and Measurement of Outcome Test Drug Control Drug Statistical Comparisons Interpretation Extrapolation 4 Study Objective/Research Question 5 STUDY OBJECTIVE • What do you want to show with this trial? • What is the population of interest? • What trial design will be used? • What are the specific study hypotheses? 6 What do you want to show with this trial? Efficacy • New drug is more efficacious than current standard therapy - active controlled superiority trial • New drug is more efficacious than placebo treatment - placebo controlled superiority trial • New drug is not worse (in terms of efficacy), by a pre-specified and clinically relevant amount, than the current therapy - active controlled non- inferiority trial – Note that there are no placebo