Module 2: Fundamentals of Epidemiology – Experimental Studies Transcript
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The Magic of Randomization Versus the Myth of Real-World Evidence
The new england journal of medicine Sounding Board The Magic of Randomization versus the Myth of Real-World Evidence Rory Collins, F.R.S., Louise Bowman, M.D., F.R.C.P., Martin Landray, Ph.D., F.R.C.P., and Richard Peto, F.R.S. Nonrandomized observational analyses of large safety and efficacy because the potential biases electronic patient databases are being promoted with respect to both can be appreciable. For ex- as an alternative to randomized clinical trials as ample, the treatment that is being assessed may a source of “real-world evidence” about the effi- well have been provided more or less often to cacy and safety of new and existing treatments.1-3 patients who had an increased or decreased risk For drugs or procedures that are already being of various health outcomes. Indeed, that is what used widely, such observational studies may in- would be expected in medical practice, since both volve exposure of large numbers of patients. the severity of the disease being treated and the Consequently, they have the potential to detect presence of other conditions may well affect the rare adverse effects that cannot plausibly be at- choice of treatment (often in ways that cannot be tributed to bias, generally because the relative reliably quantified). Even when associations of risk is large (e.g., Reye’s syndrome associated various health outcomes with a particular treat- with the use of aspirin, or rhabdomyolysis as- ment remain statistically significant after adjust- sociated with the use of statin therapy).4 Non- ment for all the known differences between pa- randomized clinical observation may also suf- tients who received it and those who did not fice to detect large beneficial effects when good receive it, these adjusted associations may still outcomes would not otherwise be expected (e.g., reflect residual confounding because of differ- control of diabetic ketoacidosis with insulin treat- ences in factors that were assessed only incom- ment, or the rapid shrinking of tumors with pletely or not at all (and therefore could not be chemotherapy). -
(Meta-Analyses of Observational Studies in Epidemiology) Checklist
MOOSE (Meta-analyses Of Observational Studies in Epidemiology) Checklist A reporting checklist for Authors, Editors, and Reviewers of Meta-analyses of Observational Studies. You must report the page number in your manuscript where you consider each of the items listed in this checklist. If you have not included this information, either revise your manuscript accordingly before submitting or note N/A. Reporting Criteria Reported (Yes/No) Reported on Page No. Reporting of Background Problem definition Hypothesis statement Description of Study Outcome(s) Type of exposure or intervention used Type of study design used Study population Reporting of Search Strategy Qualifications of searchers (eg, librarians and investigators) Search strategy, including time period included in the synthesis and keywords Effort to include all available studies, including contact with authors Databases and registries searched Search software used, name and version, including special features used (eg, explosion) Use of hand searching (eg, reference lists of obtained articles) List of citations located and those excluded, including justification Method for addressing articles published in languages other than English Method of handling abstracts and unpublished studies Description of any contact with authors Reporting of Methods Description of relevance or appropriateness of studies assembled for assessing the hypothesis to be tested Rationale for the selection and coding of data (eg, sound clinical principles or convenience) Documentation of how data were classified -
Quasi-Experimental Studies in the Fields of Infection Control and Antibiotic Resistance, Ten Years Later: a Systematic Review
HHS Public Access Author manuscript Author ManuscriptAuthor Manuscript Author Infect Control Manuscript Author Hosp Epidemiol Manuscript Author . Author manuscript; available in PMC 2019 November 12. Published in final edited form as: Infect Control Hosp Epidemiol. 2018 February ; 39(2): 170–176. doi:10.1017/ice.2017.296. Quasi-experimental Studies in the Fields of Infection Control and Antibiotic Resistance, Ten Years Later: A Systematic Review Rotana Alsaggaf, MS, Lyndsay M. O’Hara, PhD, MPH, Kristen A. Stafford, PhD, MPH, Surbhi Leekha, MBBS, MPH, Anthony D. Harris, MD, MPH, CDC Prevention Epicenters Program Department of Epidemiology and Public Health, University of Maryland School of Medicine, Baltimore, Maryland. Abstract OBJECTIVE.—A systematic review of quasi-experimental studies in the field of infectious diseases was published in 2005. The aim of this study was to assess improvements in the design and reporting of quasi-experiments 10 years after the initial review. We also aimed to report the statistical methods used to analyze quasi-experimental data. DESIGN.—Systematic review of articles published from January 1, 2013, to December 31, 2014, in 4 major infectious disease journals. METHODS.—Quasi-experimental studies focused on infection control and antibiotic resistance were identified and classified based on 4 criteria: (1) type of quasi-experimental design used, (2) justification of the use of the design, (3) use of correct nomenclature to describe the design, and (4) statistical methods used. RESULTS.—Of 2,600 articles, 173 (7%) featured a quasi-experimental design, compared to 73 of 2,320 articles (3%) in the previous review (P<.01). Moreover, 21 articles (12%) utilized a study design with a control group; 6 (3.5%) justified the use of a quasi-experimental design; and 68 (39%) identified their design using the correct nomenclature. -
Epidemiology and Biostatistics (EPBI) 1
Epidemiology and Biostatistics (EPBI) 1 Epidemiology and Biostatistics (EPBI) Courses EPBI 2219. Biostatistics and Public Health. 3 Credit Hours. This course is designed to provide students with a solid background in applied biostatistics in the field of public health. Specifically, the course includes an introduction to the application of biostatistics and a discussion of key statistical tests. Appropriate techniques to measure the extent of disease, the development of disease, and comparisons between groups in terms of the extent and development of disease are discussed. Techniques for summarizing data collected in samples are presented along with limited discussion of probability theory. Procedures for estimation and hypothesis testing are presented for means, for proportions, and for comparisons of means and proportions in two or more groups. Multivariable statistical methods are introduced but not covered extensively in this undergraduate course. Public Health majors, minors or students studying in the Public Health concentration must complete this course with a C or better. Level Registration Restrictions: May not be enrolled in one of the following Levels: Graduate. Repeatability: This course may not be repeated for additional credits. EPBI 2301. Public Health without Borders. 3 Credit Hours. Public Health without Borders is a course that will introduce you to the world of disease detectives to solve public health challenges in glocal (i.e., global and local) communities. You will learn about conducting disease investigations to support public health actions relevant to affected populations. You will discover what it takes to become a field epidemiologist through hands-on activities focused on promoting health and preventing disease in diverse populations across the globe. -
Survey Experiments
IU Workshop in Methods – 2019 Survey Experiments Testing Causality in Diverse Samples Trenton D. Mize Department of Sociology & Advanced Methodologies (AMAP) Purdue University Survey Experiments Page 1 Survey Experiments Page 2 Contents INTRODUCTION ............................................................................................................................................................................ 8 Overview .............................................................................................................................................................................. 8 What is a survey experiment? .................................................................................................................................... 9 What is an experiment?.............................................................................................................................................. 10 Independent and dependent variables ................................................................................................................. 11 Experimental Conditions ............................................................................................................................................. 12 WHY CONDUCT A SURVEY EXPERIMENT? ........................................................................................................................... 13 Internal, external, and construct validity .......................................................................................................... -
Analysis of Variance and Analysis of Variance and Design of Experiments of Experiments-I
Analysis of Variance and Design of Experimentseriments--II MODULE ––IVIV LECTURE - 19 EXPERIMENTAL DESIGNS AND THEIR ANALYSIS Dr. Shalabh Department of Mathematics and Statistics Indian Institute of Technology Kanpur 2 Design of experiment means how to design an experiment in the sense that how the observations or measurements should be obtained to answer a qqyuery inavalid, efficient and economical way. The desigggning of experiment and the analysis of obtained data are inseparable. If the experiment is designed properly keeping in mind the question, then the data generated is valid and proper analysis of data provides the valid statistical inferences. If the experiment is not well designed, the validity of the statistical inferences is questionable and may be invalid. It is important to understand first the basic terminologies used in the experimental design. Experimental unit For conducting an experiment, the experimental material is divided into smaller parts and each part is referred to as experimental unit. The experimental unit is randomly assigned to a treatment. The phrase “randomly assigned” is very important in this definition. Experiment A way of getting an answer to a question which the experimenter wants to know. Treatment Different objects or procedures which are to be compared in an experiment are called treatments. Sampling unit The object that is measured in an experiment is called the sampling unit. This may be different from the experimental unit. 3 Factor A factor is a variable defining a categorization. A factor can be fixed or random in nature. • A factor is termed as fixed factor if all the levels of interest are included in the experiment. -
The Politics of Random Assignment: Implementing Studies and Impacting Policy
The Politics of Random Assignment: Implementing Studies and Impacting Policy Judith M. Gueron Manpower Demonstration Research Corporation (MDRC) As the only nonacademic presenting a paper at this conference, I see it as my charge to focus on the challenge of implementing random assignment in the field. I will not spend time arguing for the methodological strengths of social experiments or advocating for more such field trials. Others have done so eloquently.1 But I will make my biases clear. For 25 years, I and many of my MDRC colleagues have fought to implement random assignment in diverse arenas and to show that this approach is feasible, ethical, uniquely convincing, and superior for answering certain questions. Our organization is widely credited with being one of the pioneers of this approach, and through its use producing results that are trusted across the political spectrum and that have made a powerful difference in social policy and research practice. So, I am a believer, but not, I hope, a blind one. I do not think that random assignment is a panacea or that it can address all the critical policy questions, or substitute for other types of analysis, or is always appropriate. But I do believe that it offers unique power in answering the “Does it make a difference?” question. With random assignment, you can know something with much greater certainty and, as a result, can more confidently separate fact from advocacy. This paper focuses on implementing experiments. In laying out the ingredients of success, I argue that creative and flexible research design skills are essential, but that just as important are operational and political skills, applied both to marketing the experiment in the first place and to helping interpret and promote its findings down the line. -
Guidelines for Reporting Meta-Epidemiological Methodology Research
EBM Primer Evid Based Med: first published as 10.1136/ebmed-2017-110713 on 12 July 2017. Downloaded from Guidelines for reporting meta-epidemiological methodology research Mohammad Hassan Murad, Zhen Wang 10.1136/ebmed-2017-110713 Abstract The goal is generally broad but often focuses on exam- Published research should be reported to evidence users ining the impact of certain characteristics of clinical studies on the observed effect, describing the distribu- Evidence-Based Practice with clarity and transparency that facilitate optimal tion of research evidence in a specific setting, exam- Center, Mayo Clinic, Rochester, appraisal and use of evidence and allow replication Minnesota, USA by other researchers. Guidelines for such reporting ining heterogeneity and exploring its causes, identifying are available for several types of studies but not for and describing plausible biases and providing empirical meta-epidemiological methodology studies. Meta- evidence for hypothesised associations. Unlike classic Correspondence to: epidemiological studies adopt a systematic review epidemiology, the unit of analysis for meta-epidemio- Dr Mohammad Hassan Murad, or meta-analysis approach to examine the impact logical studies is a study, not a patient. The outcomes Evidence-based Practice Center, of certain characteristics of clinical studies on the of meta-epidemiological studies are usually not clinical Mayo Clinic, 200 First Street 6–8 observed effect and provide empirical evidence for outcomes. SW, Rochester, MN 55905, USA; hypothesised associations. The unit of analysis in meta- In this guide, we adapt the items used in the PRISMA murad. mohammad@ mayo. edu 9 epidemiological studies is a study, not a patient. The statement for reporting systematic reviews and outcomes of meta-epidemiological studies are usually meta-analysis to fit the setting of meta- epidemiological not clinical outcomes. -
Simple Linear Regression: Straight Line Regression Between an Outcome Variable (Y ) and a Single Explanatory Or Predictor Variable (X)
1 Introduction to Regression \Regression" is a generic term for statistical methods that attempt to fit a model to data, in order to quantify the relationship between the dependent (outcome) variable and the predictor (independent) variable(s). Assuming it fits the data reasonable well, the estimated model may then be used either to merely describe the relationship between the two groups of variables (explanatory), or to predict new values (prediction). There are many types of regression models, here are a few most common to epidemiology: Simple Linear Regression: Straight line regression between an outcome variable (Y ) and a single explanatory or predictor variable (X). E(Y ) = α + β × X Multiple Linear Regression: Same as Simple Linear Regression, but now with possibly multiple explanatory or predictor variables. E(Y ) = α + β1 × X1 + β2 × X2 + β3 × X3 + ::: A special case is polynomial regression. 2 3 E(Y ) = α + β1 × X + β2 × X + β3 × X + ::: Generalized Linear Model: Same as Multiple Linear Regression, but with a possibly transformed Y variable. This introduces considerable flexibil- ity, as non-linear and non-normal situations can be easily handled. G(E(Y )) = α + β1 × X1 + β2 × X2 + β3 × X3 + ::: In general, the transformation function G(Y ) can take any form, but a few forms are especially common: • Taking G(Y ) = logit(Y ) describes a logistic regression model: E(Y ) log( ) = α + β × X + β × X + β × X + ::: 1 − E(Y ) 1 1 2 2 3 3 2 • Taking G(Y ) = log(Y ) is also very common, leading to Poisson regression for count data, and other so called \log-linear" models. -
Key Items to Get Right When Conducting Randomized Controlled Trials of Social Programs
Key Items to Get Right When Conducting Randomized Controlled Trials of Social Programs February 2016 This publication was produced by the Evidence-Based Policy team of the Laura and John Arnold Foundation (now Arnold Ventures). This publication is in the public domain. Authorization to reproduce it in whole or in part for educational purposes is granted. We welcome comments and suggestions on this document ([email protected]). 1 Purpose This is a checklist of key items to get right when conducting a randomized controlled trial (RCT) to evaluate a social program or practice. The checklist is designed to be a practical resource for researchers and sponsors of research. It describes items that are critical to the success of an RCT in producing valid findings about a social program’s effectiveness. This document is limited to key items, and does not address all contingencies that may affect a study’s success.1 Items in this checklist are categorized according to the following phases of an RCT: 1. Planning the study; 2. Carrying out random assignment; 3. Measuring outcomes for the study sample; and 4. Analyzing the study results. 1. Key items to get right in planning the study Choose (i) the program to be evaluated, (ii) target population for the study, and (iii) key outcomes to be measured. These should include, wherever possible, ultimate outcomes of policy importance. As illustrative examples: . An RCT of a pregnancy prevention program preferably should measure outcomes such as pregnancies or births, and not just intermediate outcomes such as condom use. An RCT of a remedial reading program preferably should measure outcomes such as reading comprehension, and not just participants’ ability to sound out words. -
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. -
A Systematic Review of Quasi-Experimental Study Designs in the Fields of Infection Control and Antibiotic Resistance
INVITED ARTICLE ANTIMICROBIAL RESISTANCE George M. Eliopoulos, Section Editor A Systematic Review of Quasi-Experimental Study Designs in the Fields of Infection Control and Antibiotic Resistance Anthony D. Harris,1,2 Ebbing Lautenbach,3,4 and Eli Perencevich1,2 1 2 Division of Health Care Outcomes Research, Department of Epidemiology and Preventive Medicine, University of Maryland School of Medicine, and Veterans Downloaded from https://academic.oup.com/cid/article/41/1/77/325424 by guest on 24 September 2021 Affairs Maryland Health Care System, Baltimore, Maryland; and 3Division of Infectious Diseases, Department of Medicine, and 4Department of Biostatistics and Epidemiology, Center for Clinical Epidemiology and Biostatistics, University of Pennsylvania School of Medicine, Philadelphia, Pennsylvania We performed a systematic review of articles published during a 2-year period in 4 journals in the field of infectious diseases to determine the extent to which the quasi-experimental study design is used to evaluate infection control and antibiotic resistance. We evaluated studies on the basis of the following criteria: type of quasi-experimental study design used, justification of the use of the design, use of correct nomenclature to describe the design, and recognition of potential limitations of the design. A total of 73 articles featured a quasi-experimental study design. Twelve (16%) were associated with a quasi-exper- imental design involving a control group. Three (4%) provided justification for the use of the quasi-experimental study design. Sixteen (22%) used correct nomenclature to describe the study. Seventeen (23%) mentioned at least 1 of the potential limitations of the use of a quasi-experimental study design.