Epidemiology Kept Simple an Introduction to Traditional and Modern Epidemiology
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Introduction to Biostatistics
Introduction to Biostatistics Jie Yang, Ph.D. Associate Professor Department of Family, Population and Preventive Medicine Director Biostatistical Consulting Core Director Biostatistics and Bioinformatics Shared Resource, Stony Brook Cancer Center In collaboration with Clinical Translational Science Center (CTSC) and the Biostatistics and Bioinformatics Shared Resource (BB-SR), Stony Brook Cancer Center (SBCC). OUTLINE What is Biostatistics What does a biostatistician do • Experiment design, clinical trial design • Descriptive and Inferential analysis • Result interpretation What you should bring while consulting with a biostatistician WHAT IS BIOSTATISTICS • The science of (bio)statistics encompasses the design of biological/clinical experiments the collection, summarization, and analysis of data from those experiments the interpretation of, and inference from, the results How to Lie with Statistics (1954) by Darrell Huff. http://www.youtube.com/watch?v=PbODigCZqL8 GOAL OF STATISTICS Sampling POPULATION Probability SAMPLE Theory Descriptive Descriptive Statistics Statistics Inference Population Sample Parameters: Inferential Statistics Statistics: 흁, 흈, 흅… 푿ഥ , 풔, 풑ෝ,… PROPERTIES OF A “GOOD” SAMPLE • Adequate sample size (statistical power) • Random selection (representative) Sampling Techniques: 1.Simple random sampling 2.Stratified sampling 3.Systematic sampling 4.Cluster sampling 5.Convenience sampling STUDY DESIGN EXPERIEMENT DESIGN Completely Randomized Design (CRD) - Randomly assign the experiment units to the treatments -
F:\RSS\Me\Society's Mathemarica
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Mathematics in the Statistical Society 1883-1933 John Aldrich No. 0919 This paper is available on our website http://www.southampton.ac.uk/socsci/economics/research/papers ISSN 0966-4246 Mathematics in the Statistical Society 1883-1933* John Aldrich Economics Division School of Social Sciences University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract This paper considers the place of mathematical methods based on probability in the work of the London (later Royal) Statistical Society in the half-century 1883-1933. The end-points are chosen because mathematical work started to appear regularly in 1883 and 1933 saw the formation of the Industrial and Agricultural Research Section– to promote these particular applications was to encourage mathematical methods. In the period three movements are distinguished, associated with major figures in the history of mathematical statistics–F. Y. Edgeworth, Karl Pearson and R. A. Fisher. The first two movements were based on the conviction that the use of mathematical methods could transform the way the Society did its traditional work in economic/social statistics while the third movement was associated with an enlargement in the scope of statistics. The study tries to synthesise research based on the Society’s archives with research on the wider history of statistics. Key names : Arthur Bowley, F. Y. Edgeworth, R. A. Fisher, Egon Pearson, Karl Pearson, Ernest Snow, John Wishart, G. Udny Yule. Keywords : History of Statistics, Royal Statistical Society, mathematical methods. -
Experimentation Science: a Process Approach for the Complete Design of an Experiment
Kansas State University Libraries New Prairie Press Conference on Applied Statistics in Agriculture 1996 - 8th Annual Conference Proceedings EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT D. D. Kratzer K. A. Ash Follow this and additional works at: https://newprairiepress.org/agstatconference Part of the Agriculture Commons, and the Applied Statistics Commons This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License. Recommended Citation Kratzer, D. D. and Ash, K. A. (1996). "EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT," Conference on Applied Statistics in Agriculture. https://doi.org/ 10.4148/2475-7772.1322 This is brought to you for free and open access by the Conferences at New Prairie Press. It has been accepted for inclusion in Conference on Applied Statistics in Agriculture by an authorized administrator of New Prairie Press. For more information, please contact [email protected]. Conference on Applied Statistics in Agriculture Kansas State University Applied Statistics in Agriculture 109 EXPERIMENTATION SCIENCE: A PROCESS APPROACH FOR THE COMPLETE DESIGN OF AN EXPERIMENT. D. D. Kratzer Ph.D., Pharmacia and Upjohn Inc., Kalamazoo MI, and K. A. Ash D.V.M., Ph.D., Town and Country Animal Hospital, Charlotte MI ABSTRACT Experimentation Science is introduced as a process through which the necessary steps of experimental design are all sufficiently addressed. Experimentation Science is defined as a nearly linear process of objective formulation, selection of experimentation unit and decision variable(s), deciding treatment, design and error structure, defining the randomization, statistical analyses and decision procedures, outlining quality control procedures for data collection, and finally analysis, presentation and interpretation of results. -
History of the Development of the ICD
History of the development of the ICD 1. Early history Sir George Knibbs, the eminent Australian statistician, credited François Bossier de Lacroix (1706-1777), better known as Sauvages, with the first attempt to classify diseases systematically (10). Sauvages' comprehensive treatise was published under the title Nosologia methodica. A contemporary of Sauvages was the great methodologist Linnaeus (1707-1778), one of whose treatises was entitled Genera morborum. At the beginning of the 19th century, the classification of disease in most general use was one by William Cullen (1710-1790), of Edinburgh, which was published in 1785 under the title Synopsis nosologiae methodicae. For all practical purposes, however, the statistical study of disease began a century earlier with the work of John Graunt on the London Bills of Mortality. The kind of classification envisaged by this pioneer is exemplified by his attempt to estimate the proportion of liveborn children who died before reaching the age of six years, no records of age at death being available. He took all deaths classed as thrush, convulsions, rickets, teeth and worms, abortives, chrysomes, infants, livergrown, and overlaid and added to them half the deaths classed as smallpox, swinepox, measles, and worms without convulsions. Despite the crudity of this classification his estimate of a 36 % mortality before the age of six years appears from later evidence to have been a good one. While three centuries have contributed something to the scientific accuracy of disease classification, there are many who doubt the usefulness of attempts to compile statistics of disease, or even causes of death, because of the difficulties of classification. -
Spanish Certificate Info and Sign up Form
International Certificate in Cultural Competency and Communication in Spanish One of the goals of Vision 2020 is to “diversify and globalize the A&M community”. Two key indicators in Texas A&M’s (TAMU) Quality Enhancement Plan are specific to creating excellence in diversity of student learning and in internationalization. These two goals are stated as follows: • Students graduating from Texas A&M University should be able to function successfully in complex, diverse, social, economic, and political contexts. • Students graduating from Texas A&M University will be able to function effectively in their chosen career fields in an international setting. Specifically, students who complete the BIMS International Certificate will: 1. Be functionally bilingual and employ attained language skills in both social and formal settings 2. Be able to perform linguistically and in a culturally sensitive manner within the medial and/or agricultural environment 3. Gain experiential knowledge abroad, expanding their cultural sensitivities and functionality in a foreign environment. Students should be able to compare and contrast Latin American and Spanish cultural ideas with those of the Anglo population within the United States. Students should recognize cultural differences, using language and social skills to interact effectively within a foreign environment. The certificate has been generated with the student’s required TAMU core curriculum in mind. Many students may wish to discuss a minor in language, political science, sociology, etc. based upon the courses taken to satisfy the certificate requirements. To discuss a minor, please speak with your academic advisor as well as the college/department in which you wish to minor. -
2–22–05 Vol. 70 No. 34 Tuesday Feb. 22, 2005 Pages 8501–8708
2–22–05 Tuesday Vol. 70 No. 34 Feb. 22, 2005 Pages 8501–8708 VerDate jul 14 2003 20:36 Feb 18, 2005 Jkt 205001 PO 00000 Frm 00001 Fmt 4710 Sfmt 4710 E:\FR\FM\22FEWS.LOC 22FEWS i II Federal Register / Vol. 70, No. 34 / Tuesday, February 22, 2005 The FEDERAL REGISTER (ISSN 0097–6326) is published daily, SUBSCRIPTIONS AND COPIES Monday through Friday, except official holidays, by the Office PUBLIC of the Federal Register, National Archives and Records Administration, Washington, DC 20408, under the Federal Register Subscriptions: Act (44 U.S.C. Ch. 15) and the regulations of the Administrative Paper or fiche 202–512–1800 Committee of the Federal Register (1 CFR Ch. I). The Assistance with public subscriptions 202–512–1806 Superintendent of Documents, U.S. Government Printing Office, Washington, DC 20402 is the exclusive distributor of the official General online information 202–512–1530; 1–888–293–6498 edition. Periodicals postage is paid at Washington, DC. Single copies/back copies: The FEDERAL REGISTER provides a uniform system for making Paper or fiche 202–512–1800 available to the public regulations and legal notices issued by Assistance with public single copies 1–866–512–1800 Federal agencies. These include Presidential proclamations and (Toll-Free) Executive Orders, Federal agency documents having general FEDERAL AGENCIES applicability and legal effect, documents required to be published Subscriptions: by act of Congress, and other Federal agency documents of public interest. Paper or fiche 202–741–6005 Documents are on file for public inspection in the Office of the Assistance with Federal agency subscriptions 202–741–6005 Federal Register the day before they are published, unless the issuing agency requests earlier filing. -
Summer 2021.Indd
Summer 2021 at | cmu.edu/osher w CONSIDER A GIFT TO OSHER To make a contribution to the Osher Annual Fund, please call the office at 412.268.7489, go through the Osher website with a credit card, or mail a check to the office. Thank you in advance for your generosity. BOARD OF DIRECTORS CURRICULUM COMMITTEE OFFICE STAFF Allan Hribar, President Stanley Winikoff (Curriculum Lyn Decker, Executive Director Jan Hawkins, Vice-President Committee Chair & SLSG) Olivia McCann, Administrator / Programs Marcia Taylor, Treasurer Gary Bates (Lecture Chair) Chelsea Prestia, Administrator / Publications Jim Reitz, Past President Les Berkowitz Kate Lehman, Administrator / General Office Ann Augustine, Secretary & John Brown Membership Chair Maureen Brown Mark Winer, Board Represtative to Flip Conti CATALOG EDITORS Executive Committee Lyn Decker (STSG) Chelsea Prestia, Editor Rosalie Barsotti Mary Duquin Jeffrey Holst Olivia McCann Anna Estop Kate Lehman Ann Isaac Marilyn Maiello Sankar Seetharama Enid Miller Raja Sooriamurthi Diane Pastorkovich CONTACT INFORMATION Jeffrey Swoger Antoinette Petrucci Osher Lifelong Learning Institute Randy Weinberg Helen-Faye Rosenblum (SLSG) Richard Wellins Carnegie Mellon University Judy Rubinstein 5000 Forbes Avenue Rochelle Steiner Pittsburgh, PA 15213-3815 Jeffrey Swoger (SLSG) Rebecca Culyba, Randy Weinberg (STSG) Associate Provost During Covid, we prefer to receive an email and University Liaison from you rather than a phone call. Please include your return address on all mail sent to the Osher office. Phone: 412.268.7489 Email: [email protected] Website: cmu.edu/osher ON THE COVER When Andrew Carnegie selected architect Henry Hornbostel to design a technical school in the late 1890s, the plan was for the layout of the buildings to form an “explorer’s ship” in search of knowledge. -
Lesson 1 Introduction to Epidemiology
Lesson 1 Introduction to Epidemiology Epidemiology is considered the basic science of public health, and with good reason. Epidemiology is: a) a quantitative basic science built on a working knowledge of probability, statistics, and sound research methods; b) a method of causal reasoning based on developing and testing hypotheses pertaining to occurrence and prevention of morbidity and mortality; and c) a tool for public health action to promote and protect the public’s health based on science, causal reasoning, and a dose of practical common sense (2). As a public health discipline, epidemiology is instilled with the spirit that epidemiologic information should be used to promote and protect the public’s health. Hence, epidemiology involves both science and public health practice. The term applied epidemiology is sometimes used to describe the application or practice of epidemiology to address public health issues. Examples of applied epidemiology include the following: • the monitoring of reports of communicable diseases in the community • the study of whether a particular dietary component influences your risk of developing cancer • evaluation of the effectiveness and impact of a cholesterol awareness program • analysis of historical trends and current data to project future public health resource needs Objectives After studying this lesson and answering the questions in the exercises, a student will be able to do the following: • Define epidemiology • Summarize the historical evolution of epidemiology • Describe the elements of a case -
Double Blind Trials Workshop
Double Blind Trials Workshop Introduction These activities demonstrate how double blind trials are run, explaining what a placebo is and how the placebo effect works, how bias is removed as far as possible and how participants and trial medicines are randomised. Curriculum Links KS3: Science SQA Access, Intermediate and KS4: Biology Higher: Biology Keywords Double-blind trials randomisation observer bias clinical trials placebo effect designing a fair trial placebo Contents Activities Materials Activity 1 Placebo Effect Activity Activity 2 Observer Bias Activity 3 Double Blind Trial Role Cards for the Double Blind Trial Activity Testing Layout Background Information Medicines undergo a number of trials before they are declared fit for use (see classroom activity on Clinical Research for details). In the trial in the second activity, pupils compare two potential new sunscreens. This type of trial is done with healthy volunteers to see if the there are any side effects and to provide data to suggest the dosage needed. If there were no current best treatment then this sort of trial would also be done with patients to test for the effectiveness of the new medicine. How do scientists make sure that medicines are tested fairly? One thing they need to do is to find out if their tests are free of bias. Are the medicines really working, or do they just appear to be working? One difficulty in designing fair tests for medicines is the placebo effect. When patients are prescribed a treatment, especially by a doctor or expert they trust, the patient’s own belief in the treatment can cause the patient to produce a response. -
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. -
Design of Experiments and Data Analysis,” 2012 Reliability and Maintainability Symposium, January, 2012
Copyright © 2012 IEEE. Reprinted, with permission, from Huairui Guo and Adamantios Mettas, “Design of Experiments and Data Analysis,” 2012 Reliability and Maintainability Symposium, January, 2012. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of ReliaSoft Corporation's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it. 2012 Annual RELIABILITY and MAINTAINABILITY Symposium Design of Experiments and Data Analysis Huairui Guo, Ph. D. & Adamantios Mettas Huairui Guo, Ph.D., CPR. Adamantios Mettas, CPR ReliaSoft Corporation ReliaSoft Corporation 1450 S. Eastside Loop 1450 S. Eastside Loop Tucson, AZ 85710 USA Tucson, AZ 85710 USA e-mail: [email protected] e-mail: [email protected] Tutorial Notes © 2012 AR&MS SUMMARY & PURPOSE Design of Experiments (DOE) is one of the most useful statistical tools in product design and testing. While many organizations benefit from designed experiments, others are getting data with little useful information and wasting resources because of experiments that have not been carefully designed. Design of Experiments can be applied in many areas including but not limited to: design comparisons, variable identification, design optimization, process control and product performance prediction. Different design types in DOE have been developed for different purposes. -
The Scientific Rationality of Early Statistics, 1833–1877
The Scientific Rationality of Early Statistics, 1833–1877 Yasuhiro Okazawa St Catharine’s College This dissertation is submitted for the degree of Doctor of Philosophy. November 2018 Declaration Declaration This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the Preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text. I further state that no substantial part of my dissertation has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the Preface and specified in the text It does not exceed the prescribed word limit of 80,000 words for the Degree Committee of the Faculty of History. Yasuhiro Okazawa 13 November 2018 i Thesis Summary The Scientific Rationality of Early Statistics, 1833–1877 Yasuhiro Okazawa Summary This thesis examines the activities of the Statistical Society of London (SSL) and its contribution to early statistics—conceived as the science of humans in society—in Britain. The SSL as a collective entity played a crucial role in the formation of early statistics, as statisticians envisaged early statistics as a collaborative scientific project and prompted large-scale observation, which required cooperation among numerous statistical observers.