A Randomized Control Trial Evaluating the Effects of Police Body-Worn
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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. -
Experimental Evidence During a Global Pandemic
Original research BMJ Glob Health: first published as 10.1136/bmjgh-2020-004222 on 23 October 2020. Downloaded from Is health politically irrelevant? Experimental evidence during a global pandemic Arnab Acharya,1 John Gerring,2 Aaron Reeves3 To cite: Acharya A, Gerring J, ABSTRACT Key questions Reeves A. Is health politically Objective To investigate how health issues affect voting irrelevant? Experimental behaviour by considering the COVID-19 pandemic, which What is already known? evidence during a global offers a unique opportunity to examine this interplay. pandemic. BMJ Global Health Political leaders in democracies are sensitive to cues Design We employ a survey experiment in which ► 2020;5:e004222. doi:10.1136/ from the electorate and are less likely to implement treatment groups are exposed to key facts about the bmjgh-2020-004222 unpopular policies. pandemic, followed by questions intended to elicit attitudes Electoral accountability is not automatic, however, toward the incumbent party and government responsibility ► Additional material is and it only exists if citizens connect specific policies ► for the pandemic. published online only. To view, to politicians and vote accordingly. Setting The survey was conducted amid the lockdown please visit the journal online ► We know little about how public health attitudes af- period of 15–26 April 2020 in three large democratic (http:// dx. doi. org/ 10. 1136/ fect voting intention or behaviour. bmjgh- 2020- 004222). countries with the common governing language of English: India, the United Kingdom and the United States. Due What are the new findings? to limitations on travel and recruitment, subjects were ► The majority of our respondents believe health is an Received 15 October 2020 recruited through the M-T urk internet platform and the important policy area and that government has some Accepted 16 October 2020 survey was administered entirely online. -
Sensitivity and Specificity. Wikipedia. Last Modified on 16 November 2013
Sensitivity and specificity - Wikipedia, the free encyclopedia Create account Log in Article Talk Read Edit View Sensitivity and specificity From Wikipedia, the free encyclopedia Main page Contents Sensitivity and specificity are statistical measures of the performance of a binary classification test, also known in statistics as Featured content classification function. Sensitivity (also called the true positive rate, or the recall rate in some fields) measures the proportion of Current events actual positives which are correctly identified as such (e.g. the percentage of sick people who are correctly identified as having Random article the condition). Specificity measures the proportion of negatives which are correctly identified as such (e.g. the percentage of Donate to Wikipedia healthy people who are correctly identified as not having the condition, sometimes called the true negative rate). These two measures are closely related to the concepts of type I and type II errors. A perfect predictor would be described as 100% Interaction sensitive (i.e. predicting all people from the sick group as sick) and 100% specific (i.e. not predicting anyone from the healthy Help group as sick); however, theoretically any predictor will possess a minimum error bound known as the Bayes error rate. About Wikipedia For any test, there is usually a trade-off between the measures. For example: in an airport security setting in which one is testing Community portal for potential threats to safety, scanners may be set to trigger on low-risk items like belt buckles and keys (low specificity), in Recent changes order to reduce the risk of missing objects that do pose a threat to the aircraft and those aboard (high sensitivity). -
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. -
Why Replications Do Not Fix the Reproducibility Crisis: a Model And
1 63 2 Why Replications Do Not Fix the Reproducibility 64 3 65 4 Crisis: A Model and Evidence from a Large-Scale 66 5 67 6 Vignette Experiment 68 7 69 8 Adam J. Berinskya,1, James N. Druckmanb,1, and Teppei Yamamotoa,1,2 70 9 71 a b 10 Department of Political Science, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, MA 02139; Department of Political Science, Northwestern 72 University, 601 University Place, Evanston, IL 60208 11 73 12 This manuscript was compiled on January 9, 2018 74 13 75 14 There has recently been a dramatic increase in concern about collective “file drawer” (4). When publication decisions depend 76 15 whether “most published research findings are false” (Ioannidis on factors beyond research quality, the emergent scientific 77 16 2005). While the extent to which this is true in different disciplines re- consensus may be skewed. Encouraging replication seems to 78 17 mains debated, less contested is the presence of “publication bias,” be one way to correct a biased record of published research 79 18 which occurs when publication decisions depend on factors beyond resulting from this file drawer problem (5–7). Yet, in the 80 19 research quality, most notably the statistical significance of an ef- current landscape, one must also consider potential publication 81 20 fect. Evidence of this “file drawer problem” and related phenom- biases at the replication stage. While this was not an issue 82 21 ena across fields abounds, suggesting that an emergent scientific for OSC since they relied on over 250 scholars to replicate the 83 22 consensus may represent false positives. -
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. -
Which Findings Should Be Published?∗
Which findings should be published?∗ Alexander Frankel† Maximilian Kasy‡ December 5, 2020 Abstract Given a scarcity of journal space, what is the optimal rule for whether an empirical finding should be published? Suppose publications inform the public about a policy-relevant state. Then journals should publish extreme results, meaning ones that move beliefs sufficiently. This optimal rule may take the form of a one- or a two-sided test comparing a point estimate to the prior mean, with critical values determined by a cost-benefit analysis. Consideration of future studies may additionally justify the publication of precise null results. If one insists that standard inference remain valid, however, publication must not select on the study’s findings. Keywords: Publication bias, mechanism design, value of information JEL Codes: C44, D80, D83 1 Introduction Not all empirical findings get published. Journals may be more likely to publish findings that are statistically significant, as documented for instance by Franco et al. (2014), Brodeur et al. (2016), and Andrews and Kasy (2019). They may also be more likely to publish findings that are surprising, or conversely ones that confirm some prior belief. Whatever its form, selective publication distorts statistical inference. If only estimates with large effect sizes were to be written up and published, say, then ∗We thank Isaiah Andrews, Victoria Baranov, Ben Brooks, Gary Chamberlain, Zoe Hitzig, Emir Kamenica, Matthew Notowidigdo, Jesse Shapiro, and Glen Weyl for helpful discussions and com- ments. †University of Chicago Booth School of Business. Address: 5807 South Woodlawn Avenue Chicago, IL 60637. E-Mail: [email protected]. ‡Department of Economics, Oxford University.