Statistician, Geneticist and Person to Only One Fisher: the Scientist
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Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists
Receiver Operating Characteristic (ROC) Curve: Practical Review for Radiologists Seong Ho Park, MD1 The receiver operating characteristic (ROC) curve, which is defined as a plot of Jin Mo Goo, MD1 test sensitivity as the y coordinate versus its 1-specificity or false positive rate Chan-Hee Jo, PhD2 (FPR) as the x coordinate, is an effective method of evaluating the performance of diagnostic tests. The purpose of this article is to provide a nonmathematical introduction to ROC analysis. Important concepts involved in the correct use and interpretation of this analysis, such as smooth and empirical ROC curves, para- metric and nonparametric methods, the area under the ROC curve and its 95% confidence interval, the sensitivity at a particular FPR, and the use of a partial area under the ROC curve are discussed. Various considerations concerning the collection of data in radiological ROC studies are briefly discussed. An introduc- tion to the software frequently used for performing ROC analyses is also present- ed. he receiver operating characteristic (ROC) curve, which is defined as a Index terms: plot of test sensitivity as the y coordinate versus its 1-specificity or false Diagnostic radiology positive rate (FPR) as the x coordinate, is an effective method of evaluat- Receiver operating characteristic T (ROC) curve ing the quality or performance of diagnostic tests, and is widely used in radiology to Software reviews evaluate the performance of many radiological tests. Although one does not necessar- Statistical analysis ily need to understand the complicated mathematical equations and theories of ROC analysis, understanding the key concepts of ROC analysis is a prerequisite for the correct use and interpretation of the results that it provides. -
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. -
Biostatistics (EHCA 5367)
The University of Texas at Tyler Executive Health Care Administration MPA Program Spring 2019 COURSE NUMBER EHCA 5367 COURSE TITLE Biostatistics INSTRUCTOR Thomas Ross, PhD INSTRUCTOR INFORMATION email - [email protected] – this one is checked once a week [email protected] – this one is checked Monday thru Friday Phone - 828.262.7479 REQUIRED TEXT: Wayne Daniel and Chad Cross, Biostatistics, 10th ed., Wiley, 2013, ISBN: 978-1- 118-30279-8 COURSE DESCRIPTION This course is designed to teach students the application of statistical methods to the decision-making process in health services administration. Emphasis is placed on understanding the principles in the meaning and use of statistical analyses. Topics discussed include a review of descriptive statistics, the standard normal distribution, sampling distributions, t-tests, ANOVA, simple and multiple regression, and chi-square analysis. Students will use Microsoft Excel or other appropriate computer software in the completion of assignments. COURSE LEARNING OBJECTIVES Upon completion of this course, the student will be able to: 1. Use descriptive statistics and graphs to describe data 2. Understand basic principles of probability, sampling distributions, and binomial, Poisson, and normal distributions 3. Formulate research questions and hypotheses 4. Run and interpret t-tests and analyses of variance (ANOVA) 5. Run and interpret regression analyses 6. Run and interpret chi-square analyses GRADING POLICY Students will be graded on homework, midterm, and final exam, see weights below. ATTENDANCE/MAKE UP POLICY All students are expected to attend all of the on-campus sessions. Students are expected to participate in all online discussions in a substantive manner. If circumstances arise that a student is unable to attend a portion of the on-campus session or any of the online discussions, special arrangements must be made with the instructor in advance for make-up activity. -
Biostatistics
Biostatistics As one of the nation’s premier centers of biostatistical research, the Department of Biostatistics is dedicated to addressing high priority public health and biomedical issues and driving scientific discovery through specialized analytic techniques and catalyzing interdisciplinary research. Our world-renowned experts are engaged in developing novel statistical methods to break new ground in current research frontiers. The Department has become a leader in mission the analysis and interpretation of complex data in genetics, brain science, clinical trials, and personalized medicine. Our mission is to improve disease prevention, In these and other areas, our faculty play key roles in diagnosis, treatment, and the public’s health by public health, mental health, social science, and biomedical developing new theoretical results and applied research as collaborators and conveners. Current research statistical methods, collaborating with scientists in is wide-ranging. designing informative experiments and analyzing their data to weigh evidence and obtain valid Examples of our work: findings, and educating the next generation of biostatisticians through an innovative and relevant • Researching efficient statistical methods using large-scale curriculum, hands-on experience, and exposure biomarker data for disease risk prediction, informing to the latest in research findings and methods. clinical trial designs, discovery of personalized treatment regimes, and guiding new and more effective interventions history • Analyzing data to -
Rothamsted in the Making of Sir Ronald Fisher Scd FRS
Rothamsted in the Making of Sir Ronald Fisher ScD FRS John Aldrich University of Southampton RSS September 2019 1 Finish 1962 “the most famous statistician and mathematical biologist in the world” dies Start 1919 29 year-old Cambridge BA in maths with no prospects takes temporary job at Rothamsted Experimental Station In between 1919-33 at Rothamsted he makes a career for himself by creating a career 2 Rothamsted helped make Fisher by establishing and elevating the office of agricultural statistician—a position in which Fisher was unsurpassed by letting him do other things—including mathematical statistics, genetics and eugenics 3 Before Fisher … (9 slides) The problems were already there viz. o the relationship between harvest and weather o design of experimental trials and the analysis of their results Leading figures in agricultural science believed the problems could be treated statistically 4 Established in 1843 by Lawes and Gilbert to investigate the effective- ness of fertilisers Sir John Bennet Lawes Sir Joseph Henry Gilbert (1814-1900) land-owner, fertiliser (1817-1901) professional magnate and amateur scientist scientist (chemist) In 1902 tired Rothamsted gets make-over when Daniel Hall becomes Director— public money feeds growth 5 Crops and weather and experimental trials Crops & the weather o examined by agriculturalists—e.g. Lawes & Gilbert 1880 o subsequently treated more by meteorologists Experimental trials a hotter topic o treated in Journal of Agricultural Science o by leading figures Wood of Cambridge and Hall -
Biostatistics (BIOSTAT) 1
Biostatistics (BIOSTAT) 1 This course covers practical aspects of conducting a population- BIOSTATISTICS (BIOSTAT) based research study. Concepts include determining a study budget, setting a timeline, identifying study team members, setting a strategy BIOSTAT 301-0 Introduction to Epidemiology (1 Unit) for recruitment and retention, developing a data collection protocol This course introduces epidemiology and its uses for population health and monitoring data collection to ensure quality control and quality research. Concepts include measures of disease occurrence, common assurance. Students will demonstrate these skills by engaging in a sources and types of data, important study designs, sources of error in quarter-long group project to draft a Manual of Operations for a new epidemiologic studies and epidemiologic methods. "mock" population study. BIOSTAT 302-0 Introduction to Biostatistics (1 Unit) BIOSTAT 429-0 Systematic Review and Meta-Analysis in the Medical This course introduces principles of biostatistics and applications Sciences (1 Unit) of statistical methods in health and medical research. Concepts This course covers statistical methods for meta-analysis. Concepts include descriptive statistics, basic probability, probability distributions, include fixed-effects and random-effects models, measures of estimation, hypothesis testing, correlation and simple linear regression. heterogeneity, prediction intervals, meta regression, power assessment, BIOSTAT 303-0 Probability (1 Unit) subgroup analysis and assessment of publication -
Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy
Review Good Statistical Practices for Contemporary Meta-Analysis: Examples Based on a Systematic Review on COVID-19 in Pregnancy Yuxi Zhao and Lifeng Lin * Department of Statistics, Florida State University, Tallahassee, FL 32306, USA; [email protected] * Correspondence: [email protected] Abstract: Systematic reviews and meta-analyses have been increasingly used to pool research find- ings from multiple studies in medical sciences. The reliability of the synthesized evidence depends highly on the methodological quality of a systematic review and meta-analysis. In recent years, several tools have been developed to guide the reporting and evidence appraisal of systematic reviews and meta-analyses, and much statistical effort has been paid to improve their methodological quality. Nevertheless, many contemporary meta-analyses continue to employ conventional statis- tical methods, which may be suboptimal compared with several alternative methods available in the evidence synthesis literature. Based on a recent systematic review on COVID-19 in pregnancy, this article provides an overview of select good practices for performing meta-analyses from sta- tistical perspectives. Specifically, we suggest meta-analysts (1) providing sufficient information of included studies, (2) providing information for reproducibility of meta-analyses, (3) using appro- priate terminologies, (4) double-checking presented results, (5) considering alternative estimators of between-study variance, (6) considering alternative confidence intervals, (7) reporting predic- Citation: Zhao, Y.; Lin, L. Good tion intervals, (8) assessing small-study effects whenever possible, and (9) considering one-stage Statistical Practices for Contemporary methods. We use worked examples to illustrate these good practices. Relevant statistical code Meta-Analysis: Examples Based on a is also provided. -
Basic Statistics
Statistics Kristof Reid Assistant Professor Medical University of South Carolina Core Curriculum V5 Financial Disclosures • None Core Curriculum V5 Further Disclosures • I am not a statistician • I do like making clinical decisions based on appropriately interpreted data Core Curriculum V5 Learning Objectives • Understand why knowing statistics is important • Understand the basic principles and statistical test • Understand common statistical errors in the medical literature Core Curriculum V5 Why should I care? Indications for statistics Core Curriculum V5 47% unable to determine study design 31% did not understand p values 38% did not understand sensitivity and specificity 83% could not use odds ratios Araoye et al, JBJS 2020;102(5):e19 Core Curriculum V5 50% (or more!) of clinical research publications have at least one statistical error Thiese et al, Journal of thoracic disease, 2016-08, Vol.8 (8), p.E726-E730 Core Curriculum V5 17% of conclusions not justified by results 39% of studies used the wrong analysis Parsons et al, J Bone Joint Surg Br. 2011;93-B(9):1154-1159. Core Curriculum V5 Are these two columns different? Column A Column B You have been asked by your insurance carrier 19 10 12 11 to prove that your total hip patient outcomes 10 9 20 19 are not statistically different than your competitor 10 17 14 10 next door. 21 19 24 13 Column A is the patient reported score for you 12 18 and column B for your competitor 17 8 12 17 17 10 9 9 How would you prove you are 24 10 21 9 18 18 different or better? 13 4 18 8 12 14 15 15 14 17 Core Curriculum V5 Are they still the same? Column A: Mean=15 Column B: Mean=12 SD=4 Core Curriculum V5 We want to know if we're making a difference Core Curriculum V5 What is statistics? a. -
Pubh 7405: REGRESSION ANALYSIS
PubH 7405: REGRESSION ANALYSIS Review #2: Simple Correlation & Regression COURSE INFORMATION • Course Information are at address: www.biostat.umn.edu/~chap/pubh7405 • On each class web page, there is a brief version of the lecture for the day – the part with “formulas”; you can review, preview, or both – and as often as you like. • Follow Reading & Homework assignments at the end of the page when & if applicable. OFFICE HOURS • Instructor’s scheduled office hours: 1:15 to 2:15 Monday & Wednesday, in A441 Mayo Building • Other times are available by appointment • When really needed, can just drop in and interrupt me; could call before coming – making sure that I’m in. • I’m at a research facility on Fridays. Variables • A variable represents a characteristic or a class of measurement. It takes on different values on different subjects/persons. Examples include weight, height, race, sex, SBP, etc. The observed values, also called “observations,” form items of a data set. • Depending on the scale of measurement, we have different types of data. There are “observed variables” (Height, Weight, etc… each takes on different values on different subjects/person) and there are “calculated variables” (Sample Mean, Sample Proportion, etc… each is a “statistic” and each takes on different values on different samples). The Standard Deviation of a calculated variable is called the Standard Error of that variable/statistic. A “variable” – sample mean, sample standard deviation, etc… included – is like a “function”; when you apply it to a target element in its domain, the result is a “number”. For example, “height” is a variable and “the height of Mrs. -
Principles of the Design and Analysis of Experiments
A FRESH LOOK AT THE BASIC PRINCIPLES OF THE DESIGN AND ANALYSIS OF EXPERIMENTS F. YATES ROTHAMSTED EXPERIMENTAL STATION, HARPENDEN 1. Introduction When Professor Neyman invited me to attend the Fifth Berkeley Symposium, and give a paper on the basic principles of the design and analysis of experiments, I was a little hesitant. I felt certain that all those here must be thoroughly conversant with these basic principles, and that to mull over them again would be of little interest. This, however, is the first symposium to be held since Sir Ronald Fisher's death, and it does therefore seem apposite that a paper discussing some aspect of his work should be given. If so, what could be better than the design and analysis of experiments, which in its modern form he created? I do not propose today to give a history of the development of the subject. This I did in a paper presented in 1963 to the Seventh International Biometrics Congress [14]. Instead I want to take a fresh look at the logical principles Fisher laid down, and the action that flows from them; also briefly to consider certain modern trends, and see how far they are really of value. 2. General principles Fisher, in his first formal exposition of experimental design [4] laid down three basic principles: replication; randomization; local control. Replication and local control (for example, arrangement in blocks or rows and columns of a square) were not new, but the idea of assigning the treatments at random (subject to the restrictions imposed by the local control) was novel, and proved to be a most fruitful contribution. -
Biostatistics and Experimental Design Spring 2014
Bio 206 Biostatistics and Experimental Design Spring 2014 COURSE DESCRIPTION Statistics is a science that involves collecting, organizing, summarizing, analyzing, and presenting numerical data. Scientists use statistics to discern patterns in natural systems and to predict how those systems will react in different situations. This course is designed to encourage an understanding and appreciation of the role of experimentation, hypothesis testing, and data analysis in the sciences. It will emphasize principles of experimental design, methods of data collection, exploratory data analysis, and the use of graphical and statistical tools commonly used by scientists to analyze data. The primary goals of this course are to help students understand how and why scientists use statistics, to provide students with the knowledge necessary to critically evaluate statistical claims, and to develop skills that students need to utilize statistical methods in their own studies. INSTRUCTOR Dr. Ann Throckmorton, Professor of Biology Office: 311 Hoyt Science Center Phone: 724-946-7209 e-mail: [email protected] Home Page: www.westminster.edu/staff/athrock Office hours: Monday 11:30 - 12:30 Wednesday 9:20 - 10:20 Thursday 12:40 - 2:00 or by appointment LECTURE 11:00 – 12:30, Tuesday/Thursday Patterson Computer Lab Attendance in lecture is expected but you will not be graded on attendance except indirectly through your grades for participation, exams, quizzes, and assignments. Because your success in this course is strongly dependent on your presence in class and your participation you should make an effort to be present at all class sessions. If you know ahead of time that you will be absent you may be able to make arrangements to attend the other section of the course. -
Epsilon-Skew-Binormal Receiver Operating Characteristic (ROC) Curves
Epsilon-skew-binormal receiver operating characteristic (ROC) curves Terry L. Mashtare Jr. Department of Biostatistics University at Bu®alo, 249 Farber Hall, 3435 Main Street, Bu®alo, NY 14214-3000, U.S.A. Roswell Park Cancer Institute, Elm & Carlton Streets, Bu®alo, NY 14263, U.S.A. Alan D. Hutson Department of Biostatistics University at Bu®alo, 249 Farber Hall, 3435 Main Street, Bu®alo, NY 14214-3000, U.S.A. April 30, 2009 Abstract In this note we extend the well-known binormal model via implementation of the epsilon-skew-normal (ESN) distribution developed by Mudholkar and Hutson (2000). We derive the equation for the receiver operating characteris- tic (ROC) curve assuming epsilon-skew-binormal (ESBN) model and examine the behavior of the maximum likelihood estimates for estimating the ESBN parameters. We then summarize the results of a simulation study to examine the asymptotic properties of the maximum likelihood estimates in the ESBN model and compare with the maximum likelihood estimates in the binormal 1 model. We also summarize the results of a simulation study comparing the two parametric models to the nonparametric ROC model. We then illustrate the maximum likelihood estimation of the ESBN model using data involving skeletal measurements in 507 physically active individuals. Keywords: AUC, diagnostic testing, prediction. 1 Introduction It is commonplace in medical studies to dichotomize a continuous predictor at a cuto® c for the purpose of diagnosing disease (yes/no). A well known method for summarizing the choice of c in terms of medical decision making is receiver operating characteristic (ROC) analysis. ROC analysis has a long history and spans parametric and non-parametric estimation methods; see [1] for some of the early references.