Randomized Controlled Trials: the Basics
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Statistical Matching: a Paradigm for Assessing the Uncertainty in the Procedure
Journal of Of®cial Statistics, Vol. 17, No. 3, 2001, pp. 407±422 Statistical Matching: A Paradigm for Assessing the Uncertainty in the Procedure Chris Moriarity1 and Fritz Scheuren2 Statistical matching has been widely used by practitioners without always adequate theoreti- cal underpinnings. The work of Kadane (1978) has been a notable exception and the present article extends his insights. Kadane's 1978 article is reprinted in this JOS issue. Modern com- puting can make possible, under techniques described here, a real advance in the application of statistical matching. Key words: Multivariate normal; complex survey designs; robustness; resampling; variance- covariance structures; and application suggestions. 1. Introduction Many government policy questions, whether on the expenditure or tax side, lend them- selves to microsimulation modeling, where ``what if'' analyses of alternative policy options are carried out (e.g., Citro and Hanushek 1991). Often, the starting point for such models, in an attempt to achieve a degree of verisimilitude, is to employ information contained in several survey microdata ®les. Typically, not all the variables wanted for the modeling have been collected together from a single individual or family. However, the separate survey ®les may have many demographic and other control variables in common. The idea arose, then, of matching the separate ®les on these common variables and thus creating a composite ®le for analysis. ``Statistical matching,'' as the technique began to be called, has been more or less widely practiced since the advent of public use ®les in the 1960's. Arguably, the desire to employ statistical matching was even an impetus for the release of several of the early public use ®les, including those involving U.S. -
A Machine Learning Approach to Census Record Linking∗
A Machine Learning Approach to Census Record Linking∗ James J. Feigenbaumy March 28, 2016 Abstract Thanks to the availability of new historical census sources and advances in record linking technology, economic historians are becoming big data geneal- ogists. Linking individuals over time and between databases has opened up new avenues for research into intergenerational mobility, the long run effects of early life conditions, assimilation, discrimination, and the returns to edu- cation. To take advantage of these new research opportunities, scholars need to be able to accurately and efficiently match historical records and produce an unbiased dataset of links for analysis. I detail a standard and transparent census matching technique for constructing linked samples that can be repli- cated across a variety of cases. The procedure applies insights from machine learning classification and text comparison to record linkage of historical data. My method teaches an algorithm to replicate how a well trained and consistent researcher would create a linked sample across sources. I begin by extracting a subset of possible matches for each record, and then use training data to tune a matching algorithm that attempts to minimize both false positives and false negatives, taking into account the inherent noise in historical records. To make the procedure precise, I trace its application to an example from my own work, linking children from the 1915 Iowa State Census to their adult-selves in the 1940 Federal Census. In addition, I provide guidance on a number of practical questions, including how large the training data needs to be relative to the sample. ∗I thank Christoph Hafemeister, Jamie Lee, Christopher Muller, Martin Rotemberg, and Nicolas Ziebarth for detailed feedback and thoughts on this project, as well as seminar participants at the NBER DAE Summer Institute and the Berkeley Demography Conference on Census Linking. -
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. -
Evolution of Clinical Trials Throughout History
Evolution of clinical trials throughout history Emma M. Nellhaus1, Todd H. Davies, PhD1 Author Affiliations: 1. Office of Research and Graduate Education, Marshall University Joan C. Edwards School of Medicine, Huntington, West Virginia The authors have no financial disclosures to declare and no conflicts of interest to report. Corresponding Author: Todd H. Davies, PhD Director of Research Development and Translation Marshall University Joan C. Edwards School of Medicine Huntington, West Virginia Email: [email protected] Abstract The history of clinical research accounts for the high ethical, scientific, and regulatory standards represented in current practice. In this review, we aim to describe the advances that grew from failures and provide a comprehensive view of how the current gold standard of clinical practice was born. This discussion of the evolution of clinical trials considers the length of time and efforts that were made in order to designate the primary objective, which is providing improved care for our patients. A gradual, historic progression of scientific methods such as comparison of interventions, randomization, blinding, and placebos in clinical trials demonstrates how these techniques are collectively responsible for a continuous advancement of clinical care. Developments over the years have been ethical as well as clinical. The Belmont Report, which many investigators lack appreciation for due to time constraints, represents the pinnacle of ethical standards and was developed due to significant misconduct. Understanding the history of clinical research may help investigators value the responsibility of conducting human subjects’ research. Keywords Clinical Trials, Clinical Research, History, Belmont Report In modern medicine, the clinical trial is the gold standard and most dominant form of clinical research. -
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. -
Stability and Median Rationalizability for Aggregate Matchings
games Article Stability and Median Rationalizability for Aggregate Matchings Federico Echenique 1, SangMok Lee 2, Matthew Shum 1 and M. Bumin Yenmez 3,* 1 Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125, USA; [email protected] (F.E.); [email protected] (M.S.) 2 Department of Economics, Washington University in St. Louis, St. Louis, MO 63130, USA; [email protected] 3 Department of Economics, Boston College, Chestnut Hill, MA 02467, USA * Correspondence: [email protected] Abstract: We develop the theory of stability for aggregate matchings used in empirical studies and establish fundamental properties of stable matchings including the result that the set of stable matchings is a non-empty, complete, and distributive lattice. Aggregate matchings are relevant as matching data in revealed preference theory. We present a result on rationalizing a matching data as the median stable matching. Keywords: aggregate matching; median stable matching; rationalizability; lattice 1. Introduction Following the seminal work of [1], an extensive literature has developed regarding matching markets with non-transferable utility. This literature assumes that there are agent-specific preferences, and studies the existence of stable matchings in which each Citation: Echenique, F.; Lee, S.; agent prefers her assigned partner to the outside option of being unmatched, and there Shum, M.; Yenmez, M.B. Stability and are no pairs of agents that would like to match with each other rather than keeping their Median Rationalizability for assigned partners. Aggregate Matchings. Games 2021, 12, In this paper, we develop the theory of stability for aggregate matchings, which we 33. -
Report on Exact and Statistical Matching Techniques
Statistical Policy Working Papers are a series of technical documents prepared under the auspices of the Office of Federal Statistical Policy and Standards. These documents are the product of working groups or task forces, as noted in the Preface to each report. These Statistical Policy Working Papers are published for the purpose of encouraging further discussion of the technical issues and to stimulate policy actions which flow from the technical findings and recommendations. Readers of Statistical Policy Working Papers are encouraged to communicate directly with the Office of Federal Statistical Policy and Standards with additional views, suggestions, or technical concerns. Office of Joseph W. Duncan Federal Statistical Director Policy Standards For sale by the Superintendent of Documents, U.S. Government Printing Office Washington, D.C. 20402 Statistical Policy Working Paper 5 Report on Exact and Statistical Matching Techniques Prepared by Subcommittee on Matching Techniques Federal Committee on Statistical Methodology DEPARTMENT OF COMMERCE UNITED STATES OF AMERICA U.S. DEPARTMENT OF COMMERCE Philip M. Klutznick Courtenay M. Slater, Chief Economist Office of Federal Statistical Policy and Standards Joseph W. Duncan, Director Issued: June 1980 Office of Federal Statistical Policy and Standards Joseph W. Duncan, Director Katherine K. Wallman, Deputy Director, Social Statistics Gaylord E. Worden, Deputy Director, Economic Statistics Maria E. Gonzalez, Chairperson, Federal Committee on Statistical Methodology Preface This working paper was prepared by the Subcommittee on Matching Techniques, Federal Committee on Statistical Methodology. The Subcommittee was chaired by Daniel B. Radner, Office of Research and Statistics, Social Security Administration, Department of Health and Human Services. Members of the Subcommittee include Rich Allen, Economics, Statistics, and Cooperatives Service (USDA); Thomas B. -
U.S. Investments in Medical and Health Research and Development 2013 - 2017 Advocacy Has Helped Bring About Five Years of Much-Needed Growth in U.S
Fall 2018 U.S. Investments in Medical and Health Research and Development 2013 - 2017 Advocacy has helped bring about five years of much-needed growth in U.S. medical and health research investment. More advocacy is critical now to ensure our nation steps up in response to health threats that we can—we must—overcome. More Than Half Favor Doubling Federal Spending on Medical Research Do you favor or oppose doubling federal spending on medical research over the next five years? 19% Not Sure 23% 8% Strongly favor Strongly oppose 16% 35% Somewhat oppose Somewhat favor Source: A Research!America survey of U.S. adults conducted in partnership with Zogby Analytics in January 2018. Research!America 3 Introduction Investment1 in medical and health research and development (R&D) in the U.S. grew by $38.8 billion or 27% from 2013 to 2017. Industry continues to invest more than any other sector, accounting for 67% of total spending in 2017, followed by the federal government at 22%. Federal investments increased from 2016 to 2017, the second year of growth after a dip from 2014 to 2015. Overall, federal investment increased by $6.1 billion or 18.4% from 2013 to 2017, but growth has been uneven across federal health agencies. Investment by other sectors, including academic and research institutions, foundations, state and local governments, and voluntary health associations and professional societies, also increased from 2013 to 2017. Looking ahead, medical and health R&D spending is expected to move in an upward trajectory in 2018 but will continue to fall short relative to the health and economic impact of major health threats. -
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 -
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 -
Clinical Research Services
Clinical Research Services Conducting efficient, innovative clinical research from initial planning to closeout WHY LEIDOS LIFE SCIENCES? In addition to developing large-scale technology programs for U.S. federal f Leidos has expertise agencies with a focus on health, Leidos provides a broad range of clinical supporting all product services and solutions to researchers, product sponsors, U.S. government development phases agencies, and other biomedical enterprises, hospitals, and health systems. for vaccines, drugs, and Our broad research experience enables us to support programs throughout biotherapeutics the development life cycle: from concept through the exploratory, f Leidos understands the development, pre-clinical, and clinical phases, as well as with product need to control clinical manufacturing and launching. Leidos designs and develops customized project scope, timelines, solutions that support groundbreaking medical research, optimize business and cost while remaining operations, and expedite the discovery of safe and effective medical flexible amidst shifting products. We apply our technical knowledge and experience in selecting research requirements institutions, clinical research organizations, and independent research f Leidos remains vendor- facilities to meet the specific needs of each clinical study. We also manage neutral while fostering and team integration, communication, and contracts throughout the project. managing collaborations Finally, Leidos has a proven track record of ensuring that research involving with