Designing an Experiment
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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 -
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. -
Data Types Are Values
Data Types Are Values James Donahue Alan Demers Data Types Are Values James Donahue Xerox Corporation Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, California 94304 Alan Demers Computer Science Department Cornell University Ithaca, New York 14853 CSL -83-5 March 1984 [P83-00005] © Copyright 1984 ACM. All rights reserved. Reprinted with permission. A bst ract: An important goal of programming language research is to isolate the fundamental concepts of languages, those basic ideas that allow us to understand the relationship among various language features. This paper examines one of these underlying notions, data type, with particular attention to the treatment of generic or polymorphic procedures and static type-checking. A version of this paper will appear in the ACM Transact~ons on Programming Languages and Systems. CR Categories and Subject Descriptors: 0.3 (Programming Languages), 0.3.1 (Formal Definitions and Theory), 0.3.3 (Language Constructs), F.3.2 (Semantics of Programming Languages) Additional Keywords and Phrases: data types, polymorphism XEROX Xerox Corporation Palo Alto Research Center 3333 Coyote Hill Road Palo Alto, California 94304 DATA TYPES ARE VALVES 1 1. Introduction An important goal of programming language research is to isolate the fundamental concepts of languages, those basic ideas that allow us to understand the relationship among various language features. This paper examines one of these underlying notions, data type, and presents a meaning for this term that allows us to: describe a simple treatment of generic or polymorphic procedures that preserves full static type-checking and allows unrestricted use of recursion; and give a precise meaning to the phrase strong typing, so that Language X is strongly typed can be interpreted as a critically important theorem about the semantics of the language. -
Ignificant Scientific Progress Is Increasingly the Result of National Or Confine Hot Plasma
ADVERTISEMENT FEATURE LIGHTING THE WAY FOR TOMORROW ignificant scientific progress is increasingly the result of national or confine hot plasma. Its maximum repeatable plasma current can reach 1MA, S international collaboration. A particular strength of the University currently the highest in the world, while its diverted plasma discharge is the of Science and Technology of China (USTC) is its powerful facilities world’s longest. designed specifically to cater for large-scale science, which will help form In 2017, EAST achieved more than 100 seconds of steady-state, the bedrock of China’s second Comprehensive National Science Center, high-confinement mode (H-mode) operation, a world record. This has based in Hefei. implications for the International Thermonuclear Experimental Reactor (ITER), a huge international effort, in which China is a participant, aiming to Accelerating research with the Hefei Light Source build the infrastructure to provide clean nuclear fusion power. USTC has proven expertise in major national projects. With USTC’s support, International collaborations through EAST have already included a joint China’s synchrotron radiation accelerator was officially launched in 1983, experiment on steady-state operation with the DIII-D team of defence becoming the country’s first dedicated VUV and soft X-ray synchrotron contractor General Atomics in the United States, joint conferences and radiation facility — the Hefei Light Source (HLS). projects on plasma-wall interactions with Germany, and joint efforts with To better serve growing demand, phase II of the HLS (HLS-II) was Russia to translate research results. launched in 2010. In this phase a linear accelerator, an 800MeV storage ring and five beamline stations were built, all of which, plus the top-off operation More fusion power: Keda Torus eXperiment mode, have significantly improved the stability, brightness and service life Another device harnessing fusion power is USTC’s Keda Torus eXperiment of the light source. -
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. -
A Randomized Control Trial Evaluating the Effects of Police Body-Worn
A randomized control trial evaluating the effects of police body-worn cameras David Yokuma,b,1,2, Anita Ravishankara,c,d,1, and Alexander Coppocke,1 aThe Lab @ DC, Office of the City Administrator, Executive Office of the Mayor, Washington, DC 20004; bThe Policy Lab, Brown University, Providence, RI 02912; cExecutive Office of the Chief of Police, Metropolitan Police Department, Washington, DC 20024; dPublic Policy and Political Science Joint PhD Program, University of Michigan, Ann Arbor, MI 48109; and eDepartment of Political Science, Yale University, New Haven, CT 06511 Edited by Susan A. Murphy, Harvard University, Cambridge, MA, and approved March 21, 2019 (received for review August 28, 2018) Police body-worn cameras (BWCs) have been widely promoted as The existing evidence on whether BWCs have the anticipated a technological mechanism to improve policing and the perceived effects on policing outcomes remains relatively limited (17–19). legitimacy of police and legal institutions, yet evidence of their Several observational studies have evaluated BWCs by compar- effectiveness is limited. To estimate the effects of BWCs, we con- ing the behavior of officers before and after the introduction of ducted a randomized controlled trial involving 2,224 Metropolitan BWCs into the police department (20, 21). Other studies com- Police Department officers in Washington, DC. Here we show pared officers who happened to wear BWCs to those without (15, that BWCs have very small and statistically insignificant effects 22, 23). The causal inferences drawn in those studies depend on on police use of force and civilian complaints, as well as other strong assumptions about whether, after statistical adjustments policing activities and judicial outcomes. -
Figureseer: Parsing Result-Figures in Research Papers
FigureSeer: Parsing Result-Figures in Research Papers Noah Siegel, Zachary Horvitz, Roie Levin, Santosh Divvala, and Ali Farhadi Allen Institute for Artificial Intelligence University of Washington http://allenai.org/plato/figureseer Fig. 1: FigureSeer is an end-to-end framework for parsing result-figures in research papers. It automatically localizes figures, classifies them, and analyses their content (center). FigureSeer enables detailed indexing, retrieval, and redesign of result-figures, such as highlighting specific results (top-left), reformatting results (bottom-left), com- plex query answering (top-right), and results summarization (bottom-right). Abstract. `Which are the pedestrian detectors that yield a precision above 95% at 25% recall?' Answering such a complex query involves identifying and analyzing the results reported in figures within several research papers. Despite the availability of excellent academic search en- gines, retrieving such information poses a cumbersome challenge today as these systems have primarily focused on understanding the text content of scholarly documents. In this paper, we introduce FigureSeer, an end- to-end framework for parsing result-figures, that enables powerful search and retrieval of results in research papers. Our proposed approach au- tomatically localizes figures from research papers, classifies them, and analyses the content of the result-figures. The key challenge in analyzing the figure content is the extraction of the plotted data and its association with the legend entries. We address this challenge by formulating a novel graph-based reasoning approach using a CNN-based similarity metric. We present a thorough evaluation on a real-word annotated dataset to demonstrate the efficacy of our approach. 1 Computer Vision for Scholarly Big Data Academic research is flourishing at an unprecedented pace. -
Randomized Controlled Trials, Development Economics and Policy Making in Developing Countries
Randomized Controlled Trials, Development Economics and Policy Making in Developing Countries Esther Duflo Department of Economics, MIT Co-Director J-PAL [Joint work with Abhijit Banerjee and Michael Kremer] Randomized controlled trials have greatly expanded in the last two decades • Randomized controlled Trials were progressively accepted as a tool for policy evaluation in the US through many battles from the 1970s to the 1990s. • In development, the rapid growth starts after the mid 1990s – Kremer et al, studies on Kenya (1994) – PROGRESA experiment (1997) • Since 2000, the growth have been very rapid. J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 2 Cameron et al (2016): RCT in development Figure 1: Number of Published RCTs 300 250 200 150 100 50 0 1975 1980 1985 1990 1995 2000 2005 2010 2015 Publication Year J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 3 BREAD Affiliates doing RCT Figure 4. Fraction of BREAD Affiliates & Fellows with 1 or more RCTs 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% 1980 or earlier 1981-1990 1991-2000 2001-2005 2006-today * Total Number of Fellows and Affiliates is 166. PhD Year J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 4 Top Journals J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 5 Many sectors, many countries J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 6 Why have RCT had so much impact? • Focus on identification of causal effects (across the board) • Assessing External Validity • Observing Unobservables • Data collection • Iterative Experimentation • Unpack impacts J-PAL | THE ROLE OF RANDOMIZED EVALUATIONS IN INFORMING POLICY 7 Focus on Identification… across the board! • The key advantage of RCT was perceived to be a clear identification advantage • With RCT, since those who received a treatment are randomly selected in a relevant sample, any difference between treatment and control must be due to the treatment • Most criticisms of experiment also focus on limits to identification (imperfect randomization, attrition, etc. -
Descriptive Statistics and ANOVA
Basic statistics Descriptive statistics and ANOVA Thomas Alexander Gerds Department of Biostatistics, University of Copenhagen Contents I Data are variable I Statistical uncertainty I Summary and display of data I Confidence intervals I ANOVA Data are variable A statistician is used to receive a value, such as 3.17 %, together with an explanation, such as "this is the expression of 1-B6.DBA-GTM in mouse 12". The value from the next mouse in the list is 4.88% . The measurement is difficult Data processing is done by humans Two mice have different genes They are exposed . and treated differently Decomposing variance Variability of data is usually a composite of I Measurement error, sampling scheme I Random variation I Genotype I Exposure, life style, environment I Treatment Statistical conclusions can often be obtained by explaining the sources of variation in the data. Example 1 In the yeast experiment of Smith and Kruglyak (2008) 1 transcript levels were profiled in 6 replicates of the same strain called ’RM’ in glucose under controlled conditions. 1the article is available at http://biology.plosjournals.org Example 1 Figure: Sources of the variation of these 6 values I Measurement error I Random variation Example 1 In the same yeast experiment Smith and Kruglyak (2008) profiled also 6 replicates of a different strain called ’By’ in glucose.The order in which the 12 samples were processed was at random to minimize a systematic experimental effect. Example 1 Figure: Sources of the variation of these 12 values I Measurement error I Study design/experimental environment I Genotype Example 1 Furthermore, Smith and Kruglyak (2008) cultured 6 ’RM’ and 6 ’By’ replicates in ethanol.The order in which the 24 samples were processed was random to minimize a systematic experimental effect. -
In Cardiovascular Epidemiology in the Early 21St Century
255 VIEWPOINT Heart: first published as 10.1136/heart.89.3.255 on 1 March 2003. Downloaded from A “natural experiment” in cardiovascular epidemiology in the early 21st century A Sekikawa, B Y Horiuchi, D Edmundowicz, H Ueshima, J D Curb, K Sutton-Tyrrell, T Okamura, T Kadowaki, A Kashiwagi, K Mitsunami, K Murata, Y Nakamura, B L Rodriguez, L H Kuller ............................................................................................................................. Heart 2003;89:255–257 Despite similar traditional risk factors, morbidity and mortality rates from coronary heart disease in western sectional study of cardiovascular disease in migrant Japanese men aged 45–69 years in and non-western cohorts remain substantially different. Hawaii, and California, and Japanese in Japan in Careful study of such cohorts may help identify novel the 1960s.5 Most of those migrated to the USA in risk factors for CHD, and contribute to the formulation of the late 19th or early 20th century, or were second generation Japanese American. By adopting new preventive strategies Americanised dietary lifestyle, the concentrations .......................................................................... of serum total cholesterol among Japanese American men in the 1960s were higher than that he term “natural experiment” is defined as: in men in Japan by almost 1.3 mmol/l. The study “Naturally occurring circumstances in which showed that the CHD mortality was significantly subsets of the population have different levels higher in Japanese American men than in men in T Japan. of exposure to a supposed causal factor, in a situ- While developed countries have witnessed a ation resembling an actual experiment where dramatic decline in CHD mortality during the human subjects would be randomly allocated to 1 20th century, it remains one of the leading causes groups.” The term is derived from the work of Dr of mortality in the western world.