Evaluation of Diagnostic Tests When There Is No Gold Standard Vol
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The Statistical Significance of Randomized Controlled Trial Results Is Frequently Fragile: a Case for a Fragility Index
The statistical significance of randomized controlled trial results is frequently fragile: A case for a Fragility Index Walsh, M., Srinathan, S. K., McAuley, D. F., Mrkobrada, M., Levine, O., Ribic, C., Molnar, A. O., Dattani, N. D., Burke, A., Guyatt, G., Thabane, L., Walter, S. D., Pogue, J., & Devereaux, P. J. (2014). The statistical significance of randomized controlled trial results is frequently fragile: A case for a Fragility Index. Journal of Clinical Epidemiology, 67(6), 622-628. https://doi.org/10.1016/j.jclinepi.2013.10.019 Published in: Journal of Clinical Epidemiology Document Version: Publisher's PDF, also known as Version of record Queen's University Belfast - Research Portal: Link to publication record in Queen's University Belfast Research Portal Publisher rights 2014 The Authors. Published by Elsevier Inc. This is an open access article published under a Creative Commons Attribution-NonCommercial-NoDerivs License (https://creativecommons.org/licenses/by-nc-nd/3.0/), which permits distribution and reproduction for non-commercial purposes, provided the author and source are cited. General rights Copyright for the publications made accessible via the Queen's University Belfast Research Portal is retained by the author(s) and / or other copyright owners and it is a condition of accessing these publications that users recognise and abide by the legal requirements associated with these rights. Take down policy The Research Portal is Queen's institutional repository that provides access to Queen's research output. Every effort has been made to ensure that content in the Research Portal does not infringe any person's rights, or applicable UK laws. -
Why Do You Need a Biostatistician? Antonia Zapf1* , Geraldine Rauch2 and Meinhard Kieser3
Zapf et al. BMC Medical Research Methodology (2020) 20:23 https://doi.org/10.1186/s12874-020-0916-4 DEBATE Open Access Why do you need a biostatistician? Antonia Zapf1* , Geraldine Rauch2 and Meinhard Kieser3 Abstract The quality of medical research importantly depends, among other aspects, on a valid statistical planning of the study, analysis of the data, and reporting of the results, which is usually guaranteed by a biostatistician. However, there are several related professions next to the biostatistician, for example epidemiologists, medical informaticians and bioinformaticians. For medical experts, it is often not clear what the differences between these professions are and how the specific role of a biostatistician can be described. For physicians involved in medical research, this is problematic because false expectations often lead to frustration on both sides. Therefore, the aim of this article is to outline the tasks and responsibilities of biostatisticians in clinical trials as well as in other fields of application in medical research. Keywords: Medical research, Biostatistician, Tasks, Responsibilities Background Biometric Society (IBS) provides a definition of biomet- What is a biostatistician, what does he or she actually do rics as a ‘field of development of statistical and mathem- and what distinguishes him or her from, for example, an atical methods applicable in the biological sciences’ [2]. epidemiologist? If we would ask this our main cooper- In here, we will focus on (human) medicine as area of ation partners like physicians or biologists, they probably application, but the results can be easily transferred to could not give a satisfying answer. This is problematic the other biological sciences like, for example, agricul- because false expectations often lead to frustration on ture or ecology. -
Should the Randomistas (Continue To) Rule?
Should the Randomistas (Continue to) Rule? Martin Ravallion Abstract The rising popularity of randomized controlled trials (RCTs) in development applications has come with continuing debates on the pros and cons of this approach. The paper revisits the issues. While RCTs have a place in the toolkit for impact evaluation, an unconditional preference for RCTs as the “gold standard” is questionable. The statistical case is unclear on a priori grounds; a stronger ethical defense is often called for; and there is a risk of distorting the evidence-base for informing policymaking. Going forward, pressing knowledge gaps should drive the questions asked and how they are answered, not the methodological preferences of some researchers. The gold standard is the best method for the question at hand. Keywords: Randomized controlled trials, bias, ethics, external validity, ethics, development policy JEL: B23, H43, O22 Working Paper 492 August 2018 www.cgdev.org Should the Randomistas (Continue to) Rule? Martin Ravallion Department of Economics, Georgetown University François Roubaud encouraged the author to write this paper. For comments the author is grateful to Sarah Baird, Mary Ann Bronson, Caitlin Brown, Kevin Donovan, Markus Goldstein, Miguel Hernan, Emmanuel Jimenez, Madhulika Khanna, Nishtha Kochhar, Andrew Leigh, David McKenzie, Berk Özler, Dina Pomeranz, Lant Pritchett, Milan Thomas, Vinod Thomas, Eva Vivalt, Dominique van de Walle and Andrew Zeitlin. Staff of the International Initiative for Impact Evaluation kindly provided an update to their database on published impact evaluations and helped with the author’s questions. Martin Ravallion, 2018. “Should the Randomistas (Continue to) Rule?.” CGD Working Paper 492. Washington, DC: Center for Global Development. -
Getting Off the Gold Standard for Causal Inference
Getting Off the Gold Standard for Causal Inference Robert Kubinec New York University Abu Dhabi March 1st, 2020 Abstract I argue that social scientific practice would be improved if we abandoned the notion that a gold standard for causal inference exists. Instead, we should consider the three main in- ference modes–experimental approaches and the counterfactual theory, large-N observational studies, and qualitative process-tracing–as observable indicators of an unobserved latent con- struct, causality. Under ideal conditions, any of these three approaches provide strong, though not perfect, evidence of an important part of what we mean by causality. I also present the use of statistical entropy, or information theory, as a possible yardstick for evaluating research designs across the silver standards. Rather than dichotomize studies as either causal or descrip- tive, the concept of relative entropy instead emphasizes the relative causal knowledge gained from a given research finding. Word Count: 9,823 words 1 You shall not crucify mankind upon a cross of gold. – William Jennings Bryan, July 8, 1896 The renewed attention to causal identification in the last twenty years has elevated the status of the randomized experiment to the sine qua non gold standard of the social sciences. Nonetheless, re- search employing observational data, both qualitative and quantitative, continues unabated, albeit with a new wrinkle: because observational data cannot, by definition, assign cases to receive causal treatments, any conclusions from these studies must be descriptive, rather than causal. However, even though this new norm has received widespread adoption in the social sciences, the way that so- cial scientists discuss and interpret their data often departs from this clean-cut approach. -
Assessing the Accuracy of a New Diagnostic Test When a Gold Standard Does Not Exist Todd A
UW Biostatistics Working Paper Series 10-1-1998 Assessing the Accuracy of a New Diagnostic Test When a Gold Standard Does Not Exist Todd A. Alonzo University of Southern California, [email protected] Margaret S. Pepe University of Washington, [email protected] Suggested Citation Alonzo, Todd A. and Pepe, Margaret S., "Assessing the Accuracy of a New Diagnostic Test When a Gold Standard Does Not Exist" (October 1998). UW Biostatistics Working Paper Series. Working Paper 156. http://biostats.bepress.com/uwbiostat/paper156 This working paper is hosted by The Berkeley Electronic Press (bepress) and may not be commercially reproduced without the permission of the copyright holder. Copyright © 2011 by the authors 1 Introduction Medical diagnostic tests play an important role in health care. New diagnostic tests for detecting viral and bacterial infections are continually being developed. The performance of a new diagnostic test is ideally evaluated by comparison with a perfect gold standard test (GS) which assesses infection status with certainty. Many times a perfect gold standard does not exist and a reference test or imperfect gold standard must be used instead. Although statistical methods and techniques are well established for assessing the accuracy of a new diagnostic test when a perfect gold standard exists, such methods are lacking for settings where a gold standard is not available. In this paper we will review some existing approaches to this problem and propose an alternative approach. To fix ideas we consider a specific example. Chlamydia trachomatis is the most common sexually transmitted bacterial pathogen. In women Chlamydia trachomatis can result in pelvic inflammatory disease (PID) which can lead to infertility, chronic pelvic pain, and life-threatening ectopic pregnancy. -
Memorandum Explaining Basis for Declining Request for Emergency Use Authorization for Emergency Use of Hydroxychloroquine Sulfate
Memorandum Explaining Basis for Declining Request for Emergency Use Authorization for Emergency Use of Hydroxychloroquine Sulfate On July 6, 2020, the United States Food and Drug Administration (FDA) received a submission from Dr. William W. O’Neill, co-signed by Dr. John E. McKinnon1, Dr. Dee Dee Wang, and Dr. Marcus J. Zervos requesting, among other things, emergency use authorization (EUA) of hydroxychloroquine sulfate (HCQ) for prevention (pre- and post-exposure prophylaxis) and treatment of “early COVID-19 infections”. We interpret the term “early COVID-19 infections” to mean individuals with 2019 coronavirus disease (COVID-19) who are asymptomatic or pre- symptomatic or have mild illness.2 The statutory criteria for issuing an EUA are set forth in Section 564(c) of the Federal Food, Drug and Cosmetic Act (FD&C Act). Specifically, the FDA must determine, among other things, that “based on the totality of scientific information available to [FDA], including data from adequate and well-controlled clinical trials, if available,” it is reasonable to believe that the product may be effective in diagnosing, treating, or preventing a serious or life-threatening disease or condition caused by the chemical, biological, radiological, or nuclear agent; that the known and potential benefits, when used to diagnose, treat, or prevent such disease or condition, outweigh the known and potential risks of the product; and that there are no adequate, approved, and available alternatives. FDA scientific review staff have reviewed available information derived from clinical trials and observational studies investigating the use of HCQ in the prevention or the treatment of COVID- 19. -
Medical Statistics PDF File 9
M249 Practical modern statistics Medical statistics About this module M249 Practical modern statistics uses the software packages IBM SPSS Statistics (SPSS Inc.) and WinBUGS, and other software. This software is provided as part of the module, and its use is covered in the Introduction to statistical modelling and in the four computer books associated with Books 1 to 4. This publication forms part of an Open University module. Details of this and other Open University modules can be obtained from the Student Registration and Enquiry Service, The Open University, PO Box 197, Milton Keynes MK7 6BJ, United Kingdom (tel. +44 (0)845 300 60 90; email [email protected]). Alternatively, you may visit the Open University website at www.open.ac.uk where you can learn more about the wide range of modules and packs offered at all levels by The Open University. To purchase a selection of Open University materials visit www.ouw.co.uk, or contact Open University Worldwide, Walton Hall, Milton Keynes MK7 6AA, United Kingdom for a brochure (tel. +44 (0)1908 858779; fax +44 (0)1908 858787; email [email protected]). Note to reader Mathematical/statistical content at the Open University is usually provided to students in printed books, with PDFs of the same online. This format ensures that mathematical notation is presented accurately and clearly. The PDF of this extract thus shows the content exactly as it would be seen by an Open University student. Please note that the PDF may contain references to other parts of the module and/or to software or audio-visual components of the module. -
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. -
Evaluating Diagnostic Tests in the Absence of a Gold Standard
Evaluating Diagnostic Tests in the Absence of a Gold Standard Nandini Dendukuri Departments of Medicine & Epidemiology, Biostatistics and Occupational Health, McGill University; Technology Assessment Unit, McGill University Health Centre Advanced TB diagnostics course, Montreal, July 2011 Evaluating Diagnostic Tests in the Absence of a Gold Standard • An area where we are still awaiting a solution – We are still working on methods for Phase 0 studies • A number of methods have appeared in the pipeline – Some have been completely discredited (e.g. Discrepant Analysis) – Some are more realistic but have had scale-up problems due to mathematical complexity and lack of software (e.g. Latent Class Analysis) – Some in-between solutions seem easy to use, but their inherent biases are poorly understood (e.g. Composite reference standards) • Not yet at the stage where you can stick your data into a machine and get accurate results in 2 hours No gold-standard for many types of TB • Example 1: TB pleuritis: – Conventional tests have less than perfect sensitivity* Microscopy of the pleural fluid <5% Culture of pleural fluid 24 to 58% Biopsy of pleural tissue ~ 86% + culture of biopsy material – Most conventional tests have good, though not perfect specificity ranging from 90-100% * Source: Pai et al., BMC Infectious Diseases, 2004 No gold-standard for many types of TB • Example 2: Latent TB Screening/Diagnosis: – Traditionally based on Tuberculin Skin Test (TST) • TST has poor specificity* due to cross-reactivity with BCG vaccination and infection -
The Use of Stata in Medical Statistics and Epidemiology: a Long Journey
A retrospective view Stata Progress Epidemiology and Biostatistics with Stata My Journey with Stata The use of Stata in Medical Statistics and Epidemiology: a long journey Rino Bellocco, Sc.D. Department of Statistics and Quantitative Methods University of Milano-Bicocca & Department of Medical Epidemiology and Biostatistics Karolinska Institutet Nordic and Baltic Stata Users Group meeting, Stockholm, September 1, 2017 1 / 44 A retrospective view Stata Progress Epidemiology and Biostatistics with Stata My Journey with Stata Outline A retrospective view Stata Progress Epidemiology and Biostatistics with Stata My Journey with Stata 2 / 44 A retrospective view Stata Progress Epidemiology and Biostatistics with Stata My Journey with Stata Outline A retrospective view Stata Progress Epidemiology and Biostatistics with Stata My Journey with Stata 3 / 44 A retrospective view Stata Progress Epidemiology and Biostatistics with Stata My Journey with Stata Some History 1985-1995 • Version 1.0 - 1.5, January 1985 - February 1987 (around 48 commands, regress logit ) 4 / 44 N. J. Cox 15 Table 2: Releases of Stata 1.0 January 1985 3.1 August 1993 1.1 February 1985 4.0 January 1995 1.2 March 1985 5.0 October 1996 1.4 August 1986 6.0 January 1999 1.5 February 1987 7.0 December 2000 2.0 June 1988 8.0 January 2003 A retrospective view Stata2.05 Progress June 1989Epidemiology and Biostatistics8.1 July with 2003 Stata My Journey with Stata 2.1 September 1990 8.2 October 2003 3.0 MarchBasic 1992 Commands Table 3: Stata 1.0 and Stata 1.1 append dir infile plot spool beep do input query summarize by drop label regress tabulate capture erase list rename test confirm exit macro replace type convert expand merge run use correlate format modify save count generate more set describe help outfile sort Stata 1.0 had int, long, float,anddouble variables; it did not have byte or str#. -
Screening for Alcohol Problems, What Makes a Test Effective?
Screening for Alcohol Problems What Makes a Test Effective? Scott H. Stewart, M.D., and Gerard J. Connors, Ph.D. Screening tests are useful in a variety of settings and contexts, but not all disorders are amenable to screening. Alcohol use disorders (AUDs) and other drinking problems are a major cause of morbidity and mortality and are prevalent in the population; effective treatments are available, and patient outcome can be improved by early detection and intervention. Therefore, the use of screening tests to identify people with or at risk for AUDs can be beneficial. The characteristics of screening tests that influence their usefulness in clinical settings include their validity, sensitivity, and specificity. Appropriately conducted screening tests can help clinicians better predict the probability that individual patients do or do not have a given disorder. This is accomplished by qualitatively or quantitatively estimating variables such as positive and negative predictive values of screening in a population, and by determining the probability that a given person has a certain disorder based on his or her screening results. KEY WORDS: AOD (alcohol and other drug) use screening method; identification and screening for AODD (alcohol and other drug disorders); risk assessment; specificity of measurement; sensitivity of measurement; predictive validity; Alcohol Use Disorders Identification Test (AUDIT) he term “screening” refers to the confirm whether or not they have the application of a test to members disorder. When a screening test indicates SCOTT H. STEWART, M.D., is an assistant Tof a population (e.g., all patients that a patient may have an AUD or professor in the Department of Medicine, in a physician’s practice) to estimate their other drinking problem, the clinician School of Medicine and Biomedical probability of having a specific disorder, might initiate a brief intervention and Sciences at the State University of New such as an alcohol use disorder (AUD) arrange for clinical followup, which York at Buffalo, Buffalo, New York. -
Medical Statistics Made Easy Medical Statistics Made Easy M
MEDICAL STATISTICS MADE EASY STATISTICS MEDICAL MEDICAL STATISTICS MADE EASY M. HARRIS and G. TAYLOR 4th EDITION HARRIS and TAYLOR 4 MEDICAL STATISTICS MADE EASY 4 MSME_Book.indb 1 17/08/2020 12:10 MSME_4e_234x156_Prelims.indd 1 06/05/2020 16:02 MEDICAL STATISTICS MADE EASY 4 Michael Harris Professor of Primary Care and former General Practitioner, Bath, UK and Gordon Taylor Professor of Medical Statistics, College of Medicine and Health, University of Exeter, UK MSME_Book.indb 3 17/08/2020 12:10 MSME_4e_234x156_Prelims.indd 3 06/05/2020 16:02 Fourth edition © Scion Publishing Ltd, 2021 ISBN 978 1 911510 63 5 Third edition published in 2014 by Scion Publishing Ltd (978 1 907904 03 5) Second edition published in 2008 by Scion Publishing Ltd (978 1 904842 55 2) First edition published in 2003 by Martin Dunitz (1 85996 219 X) Scion Publishing Limited The Old Hayloft, Vantage Business Park, Bloxham Road, Banbury OX16 9UX, UK www.scionpublishing.com Important Note from the Publisher The information contained within this book was obtained by Scion Publishing Ltd from sources believed by us to be reliable. However, while every effort has been made to ensure its accuracy, no responsibility for loss or injury whatsoever occasioned to any person acting or refraining from action as a result of information contained herein can be accepted by the authors or publishers. Readers are reminded that medicine is a constantly evolving science and while the authors and publishers have ensured that all dosages, applications and practices are based on current indications, there may be specific practices which differ between communities.