The Existence of Publication Bias and Risk Factors for Its Occurrence
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Pat Croskerry MD Phd
Thinking (and the factors that influence it) Pat Croskerry MD PhD Scottish Intensive Care Society St Andrews, January 2011 RECOGNIZED Intuition Pattern Pattern Initial Recognition Executive Dysrationalia Calibration Output information Processor override T override Repetition NOT Analytical RECOGNIZED reasoning Dual Process Model Medical Decision Making Intuitive Analytical Orthodox Medical Decision Making (Analytical) Rational Medical Decision-Making • Knowledge base • Differential diagnosis • Best evidence • Reviews, meta-analysis • Biostatistics • Publication bias, citation bias • Test selection and interpretation • Bayesian reasoning • Hypothetico-deductive reasoning .„Cognitive thought is the tip of an enormous iceberg. It is the rule of thumb among cognitive scientists that unconscious thought is 95% of all thought – .this 95% below the surface of conscious awareness shapes and structures all conscious thought‟ Lakoff and Johnson, 1999 Rational blind-spots • Framing • Context • Ambient conditions • Individual factors Individual Factors • Knowledge • Intellect • Personality • Critical thinking ability • Decision making style • Gender • Ageing • Circadian type • Affective state • Fatigue, sleep deprivation, sleep debt • Cognitive load tolerance • Susceptibility to group pressures • Deference to authority Intelligence • Measurement of intelligence? • IQ most widely used barometer of intellect and cognitive functioning • IQ is strongest single predictor of job performance and success • IQ tests highly correlated with each other • Population -
Cognitive Biases in Software Engineering: a Systematic Mapping Study
Cognitive Biases in Software Engineering: A Systematic Mapping Study Rahul Mohanani, Iflaah Salman, Burak Turhan, Member, IEEE, Pilar Rodriguez and Paul Ralph Abstract—One source of software project challenges and failures is the systematic errors introduced by human cognitive biases. Although extensively explored in cognitive psychology, investigations concerning cognitive biases have only recently gained popularity in software engineering research. This paper therefore systematically maps, aggregates and synthesizes the literature on cognitive biases in software engineering to generate a comprehensive body of knowledge, understand state of the art research and provide guidelines for future research and practise. Focusing on bias antecedents, effects and mitigation techniques, we identified 65 articles (published between 1990 and 2016), which investigate 37 cognitive biases. Despite strong and increasing interest, the results reveal a scarcity of research on mitigation techniques and poor theoretical foundations in understanding and interpreting cognitive biases. Although bias-related research has generated many new insights in the software engineering community, specific bias mitigation techniques are still needed for software professionals to overcome the deleterious effects of cognitive biases on their work. Index Terms—Antecedents of cognitive bias. cognitive bias. debiasing, effects of cognitive bias. software engineering, systematic mapping. 1 INTRODUCTION OGNITIVE biases are systematic deviations from op- knowledge. No analogous review of SE research exists. The timal reasoning [1], [2]. In other words, they are re- purpose of this study is therefore as follows: curring errors in thinking, or patterns of bad judgment Purpose: to review, summarize and synthesize the current observable in different people and contexts. A well-known state of software engineering research involving cognitive example is confirmation bias—the tendency to pay more at- biases. -
Opportunities for Selective Reporting of Harms in Randomized Clinical Trials: Selection Criteria for Nonsystematic Adverse Events
Opportunities for selective reporting of harms in randomized clinical trials: Selection criteria for nonsystematic adverse events Evan Mayo-Wilson ( [email protected] ) Johns Hopkins University Bloomberg School of Public Health https://orcid.org/0000-0001-6126-2459 Nicole Fusco Johns Hopkins University Bloomberg School of Public Health Hwanhee Hong Duke University Tianjing Li Johns Hopkins University Bloomberg School of Public Health Joseph K. Canner Johns Hopkins University School of Medicine Kay Dickersin Johns Hopkins University Bloomberg School of Public Health Research Article Keywords: Harms, adverse events, clinical trials, reporting bias, selective outcome reporting, data sharing, trial registration Posted Date: February 5th, 2019 DOI: https://doi.org/10.21203/rs.2.268/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License Version of Record: A version of this preprint was published on September 5th, 2019. See the published version at https://doi.org/10.1186/s13063-019-3581-3. Page 1/16 Abstract Background: Adverse events (AEs) in randomized clinical trials may be reported in multiple sources. Different methods for reporting adverse events across trials, or across sources for a single trial, may produce inconsistent and confusing information about the adverse events associated with interventions Methods: We sought to compare the methods authors use to decide which AEs to include in a particular source (i.e., “selection criteria”) and to determine how selection criteria could impact the AEs reported. We compared sources (e.g., journal articles, clinical study reports [CSRs]) of trials for two drug-indications: gabapentin for neuropathic pain and quetiapine for bipolar depression. -
Why Too Many Political Science Findings Cannot
www.ssoar.info Why Too Many Political Science Findings Cannot be Trusted and What We Can Do About it: A Review of Meta-scientific Research and a Call for Institutional Reform Wuttke, Alexander Preprint / Preprint Zeitschriftenartikel / journal article Empfohlene Zitierung / Suggested Citation: Wuttke, A. (2019). Why Too Many Political Science Findings Cannot be Trusted and What We Can Do About it: A Review of Meta-scientific Research and a Call for Institutional Reform. Politische Vierteljahresschrift, 60(1). https:// doi.org/10.1007/s11615-018-0131-7 Nutzungsbedingungen: Terms of use: Dieser Text wird unter einer CC BY-NC Lizenz (Namensnennung- This document is made available under a CC BY-NC Licence Nicht-kommerziell) zur Verfügung gestellt. Nähere Auskünfte zu (Attribution-NonCommercial). For more Information see: den CC-Lizenzen finden Sie hier: https://creativecommons.org/licenses/by-nc/4.0 https://creativecommons.org/licenses/by-nc/4.0/deed.de Diese Version ist zitierbar unter / This version is citable under: https://nbn-resolving.org/urn:nbn:de:0168-ssoar-59909-5 Wuttke (2019): Credibility of Political Science Findings Why Too Many Political Science Findings Cannot be Trusted and What We Can Do About it: A Review of Meta-scientific Research and a Call for Institutional Reform Alexander Wuttke, University of Mannheim 2019, Politische Vierteljahresschrift / German Political Science Quarterly 1, 60: 1-22, DOI: 10.1007/s11615-018-0131-7. This is an uncorrected pre-print. Please cite the original article. Witnessing the ongoing “credibility revolutions” in other disciplines, also political science should engage in meta-scientific introspection. Theoretically, this commentary describes why scientists in academia’s current incentive system work against their self-interest if they prioritize research credibility. -
Systematic Reviews in Health Care: Meta-Analysis in Context
© BMJ Publishing Group 2001 Chapter 4 © Crown copyright 2000 Chapter 24 © Crown copyright 1995, 2000 Chapters 25 and 26 © The Cochrane Collaboration 2000 All rights reserved. No part of this publication may be reproduced, stored in a retrieval system, or transmitted, in any form or by any means, electronic, mechanical, photocopy- ing, recording and/or otherwise, without the prior written permission of the publishers. First published in 1995 by the BMJ Publishing Group, BMA House, Tavistock Square, London WC1H 9JR www.bmjbooks.com First edition 1995 Second impression 1997 Second edition 2001 British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN 0-7279-1488–X Typeset by Phoenix Photosetting, Chatham, Kent Printed and bound by MPG Books, Bodmin, Cornwall Contents Contributors viii Foreword xiii Introduction 1 Rationale, potentials, and promise of systematic reviews 3 MATTHIAS EGGER, GEORGE DAVEY SMITH, KEITH O’ROURKE Part I: Systematic reviews of controlled trials 2 Principles of and procedures for systematic reviews 23 MATTHIAS EGGER, GEORGE DAVEY SMITH 3 Problems and limitations in conducting systematic reviews 43 MATTHIAS EGGER, KAY DICKERSIN, GEORGE DAVEY SMITH 4 Identifying randomised trials 69 CAROL LEFEBVRE, MICHAEL JCLARKE 5 Assessing the quality of randomised controlled trials 87 PETER JÜNI, DOUGLAS G ALTMAN, MATTHIAS EGGER 6 Obtaining individual patient data from randomised controlled trials 109 MICHAEL J CLARKE, LESLEY A STEWART 7 Assessing the quality of reports -
Understanding the Replication Crisis As a Base Rate Fallacy
Understanding the Replication Crisis as a Base-Rate Fallacy PhilInBioMed, Université de Bordeaux 6 June 2018 [email protected] introduction — the replication crisis 52% of 1,576 scientists taking a survey conducted by the journal Nature agreed that there was a significant crisis of reproducibility Amgen successfully replicated only 6 out of 53 studies in oncology And then there is social psychology . introduction — the base rate fallacy screening for a disease, which affects 1 in every 1,000 individuals, with a 95% accurate test an individual S tests positive, no other risk factors; what is the probability that S has the disease? Harvard medical students, 1978 11 out of 60 got the correct answer introduction — the base rate fallacy introduction — the base rate fallacy base rate of disease = 1 in 1,000 = 0.1% (call this π) false positive rate = 5% (call this α) false positives among the 999 disease-free greatly outnumber the 1 true positive from the base rate fallacy to the replication crisis two types of error and accuracy type of error error rate accuracy type of accuracy Type-I (false +ve) α 1– α confidence level Type-II (false −ve) β 1– β power from the base rate fallacy to the replication crisis do not conflate False Positive Report Probability (FPRP) Pr (S does not have the disease, given that S tests positive for the disease) with False positive error rate (α) Pr (S tests positive for the disease, given that S does not have the disease) from the base rate fallacy to the replication crisis do not conflate Pr (the temperature will -
Opportunities and Challenges of Using Systematic Reviews to Summarize
Opportunities and challenges of using systematic reviews to summarize knowledge about “what works” in disease prevention & health promotion Kay Dickersin, MA, PhD NIH Office Of Disease Prevention Rockville, Maryland July 25, 2016 Kay Dickersin’s declaration of interests • Grants and contracts from agencies: – NIH-Cochrane Eyes and Vision – PCORI-Influence of multiple sources of data on meta- analysis – PCORI-Engagement of consumers – PCORI-Consumer Summit with G-I-N North America – AHRQ-Consumers United for Evidence-based Healthcare Conference Grant – FDA-Centers for Excellence in Regulatory Science Innovation (GC Alexander, PI) SEND QUESTIONS TO [email protected] USE @NIHPREVENTS & #NIHMTG ON TWITTER Reviews are necessary in health and healthcare • Systematic reviews of existing research scientifically summarize “what works” at any point in time. • Reasons for summarizing what works vary (e.g., understanding priorities for research, pursuing answers where there are knowledge gaps, or setting guidelines for care) SEND QUESTIONS TO [email protected] USE @NIHPREVENTS & #NIHMTG ON TWITTER What is a systematic review? • A review of existing knowledge that uses explicit, scientific methods. • Systematic reviews may also combine results quantitatively (“meta-analysis”) SEND QUESTIONS TO [email protected] USE @NIHPREVENTS & #NIHMTG ON TWITTER Types of review articles Individual patient data (IPD) meta- Systematic analyses reviews with meta-analyses Reviews that are not systematic Systematic (traditional, reviews All reviews -
A Meta-Meta-Analysis
Journal of Intelligence Article Effect Sizes, Power, and Biases in Intelligence Research: A Meta-Meta-Analysis Michèle B. Nuijten 1,* , Marcel A. L. M. van Assen 1,2, Hilde E. M. Augusteijn 1, Elise A. V. Crompvoets 1 and Jelte M. Wicherts 1 1 Department of Methodology & Statistics, Tilburg School of Social and Behavioral Sciences, Tilburg University, Warandelaan 2, 5037 AB Tilburg, The Netherlands; [email protected] (M.A.L.M.v.A.); [email protected] (H.E.M.A.); [email protected] (E.A.V.C.); [email protected] (J.M.W.) 2 Section Sociology, Faculty of Social and Behavioral Sciences, Utrecht University, Heidelberglaan 1, 3584 CS Utrecht, The Netherlands * Correspondence: [email protected]; Tel.: +31-13-466-2053 Received: 7 May 2020; Accepted: 24 September 2020; Published: 2 October 2020 Abstract: In this meta-study, we analyzed 2442 effect sizes from 131 meta-analyses in intelligence research, published from 1984 to 2014, to estimate the average effect size, median power, and evidence for bias. We found that the average effect size in intelligence research was a Pearson’s correlation of 0.26, and the median sample size was 60. Furthermore, across primary studies, we found a median power of 11.9% to detect a small effect, 54.5% to detect a medium effect, and 93.9% to detect a large effect. We documented differences in average effect size and median estimated power between different types of intelligence studies (correlational studies, studies of group differences, experiments, toxicology, and behavior genetics). -
Protecting Against Researcher Bias in Secondary Data Analysis
Protecting against researcher bias in secondary data analysis: Challenges and potential solutions Jessie R. Baldwin1,2, PhD, Jean-Baptiste Pingault1,2, PhD, Tabea Schoeler,1 PhD, Hannah M. Sallis3,4,5, PhD & Marcus R. Munafò3,4,6, PhD 4,618 words; 2 tables; 1 figure 1 Department of Clinical, Educational and Health Psychology, Division of Psychology and Language Sciences, University College London, London, UK 2 Social, Genetic and Developmental Psychiatry Centre, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London, UK 3 MRC Integrative Epidemiology Unit at the University of Bristol, Bristol Medical School, University of Bristol, Bristol, UK 4 School of Psychological Science, University of Bristol, Bristol, UK 5 Centre for Academic Mental Health, Population Health Sciences, University of Bristol, Bristol, UK 6 NIHR Biomedical Research Centre, University Hospitals Bristol NHS Foundation Trust and University of Bristol, Bristol, UK Correspondence: Dr. Jessie R. Baldwin, Department of Clinical, Educational and Health Psychology, Division of Psychology and Language Sciences, University College London, London, WC1H 0AP, UK; [email protected] Funding: J.R.B is funded by a Wellcome Trust Sir Henry Wellcome fellowship (grant 215917/Z/19/Z). J.B.P is a supported by the Medical Research Foundation 2018 Emerging Leaders 1st Prize in Adolescent Mental Health (MRF-160-0002-ELP-PINGA). M.R.M and H.M.S work in a unit that receives funding from the University of Bristol and the UK Medical Research Council (MC_UU_00011/5, MC_UU_00011/7), and M.R.M is also supported by the National Institute for Health Research (NIHR) Biomedical Research Centre at the University Hospitals Bristol National Health Service Foundation Trust and the University of Bristol. -
Threats of a Replication Crisis in Empirical Computer Science Andy Cockburn, Pierre Dragicevic, Lonni Besançon, Carl Gutwin
Threats of a replication crisis in empirical computer science Andy Cockburn, Pierre Dragicevic, Lonni Besançon, Carl Gutwin To cite this version: Andy Cockburn, Pierre Dragicevic, Lonni Besançon, Carl Gutwin. Threats of a replication crisis in empirical computer science. Communications of the ACM, Association for Computing Machinery, 2020, 63 (8), pp.70-79. 10.1145/3360311. hal-02907143 HAL Id: hal-02907143 https://hal.inria.fr/hal-02907143 Submitted on 27 Jul 2020 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Threats of a Replication Crisis in Empirical Computer Science Andy Cockburn1 Pierre Dragicevic2 Lonni Besançon3 Carl Gutwin4 1University of Canterbury, New Zealand 2Inria, Université Paris-Saclay, France 3Linköping University, Sweden 4University of Saskatchewan, Canada This is the authors’ own version. The final version is available at https://doi.org/10.1145/3360311 Key insights: • Many areas of computer science research (e.g., performance analysis, software engineering, arti- ficial intelligence, and human-computer interaction) validate research claims by using statistical significance as the standard of evidence. • A loss of confidence in statistically significant findings is plaguing other empirical disciplines, yet there has been relatively little debate of this issue and its associated ‘replication crisis’ in computer science. -
Publication Bias
CHAPTER 30 Publication Bias Introduction The problem of missing studies Methods for addressing bias Illustrative example The model Getting a sense of the data Is there evidence of any bias? Is the entire effect an artifact of bias? How much of an impact might the bias have? Summary of the findings for the illustrative example Some important caveats Small-study effects Concluding remarks INTRODUCTION While a meta-analysis will yield a mathematically accurate synthesis of the studies included in the analysis, if these studies are a biased sample of all relevant studies, then the mean effect computed by the meta-analysis will reflect this bias. Several lines of evidence show that studies that report relatively high effect sizes are more likely to be published than studies that report lower effect sizes. Since published studies are more likely to find their way into a meta-analysis, any bias in the literature is likely to be reflected in the meta-analysis as well. This issue is generally known as publication bias. The problem of publication bias is not unique to systematic reviews. It affects the researcher who writes a narrative review and even the clinician who is searching a database for primary papers. Nevertheless, it has received more attention with regard to systematic reviews and meta-analyses, possibly because these are pro- moted as being more accurate than other approaches to synthesizing research. In this chapter we first discuss the reasons for publication bias and the evidence that it exists. Then we discuss a series of methods that have been developed to assess Introduction to Meta-Analysis. -
Mtg2016-Dickersin-References.Pdf
References for NIH Office of Disease Prevention webinar July 25, 2016 Kay Dickersin Slides 1. Pai M, McCulloch M, Gorman JD, et al/ Systematic reviews and meta-analyses. an illustrated, step-by-step guide/ Natl Med J India 2004-17(2).86-95/ 2. Institute of Medicine. Finding what works in health care: standards for systematic reviews. March 23, 2011. Available at: http://www.iom.edu/Reports/2011/Finding-What-Works-in-Health-Care-Standards-for Systematic-Reviews.aspx 3. Institute of Medicine. Clinical practice guidelines we can trust. March 23, 2011. Available at: http://www.nationalacademies.org/hmd/Reports/2011/Clinical-Practice-Guidelines-We Can-Trust.aspx 4. Higgins JPT, Green S. Cochrane handbook for systematic reviews of interventions (version 5.1.0). Available at: http://www.cochrane-handbook.org/ 5. Dijkers, M. KT Update (Vol. 4, No. 1 – December 2015) Available at: http://ktdrr.org/products/update/v4n1 6. Tricco !, Soobiah C, !ntony J, Cogo E, MacDonald H, Lillie E, Tran J, D’Souza J, Hui W, Perrier L, Welch V, Horsley T, Straus SE, Kastner M. A scoping review identifies multiple emerging knowledge synthesis methods, but few studies operationalize the method. Journal of Clinical Epidemiology 73: 19e28. Published online: February 15, 2016. DOI: http://dx.doi.org/10.1016/j.jclinepi.2015.08.030 7. Chandler J, Churchill R, Higgins J, Tovey D. Methodological standards for the conduct of new Cochrane Intervention Reviews. Version 2.2. 17 December 2012 – Available at: http://www.editorial-unit.cochrane.org/sites/editorial unit.cochrane.org/files/uploads/MECIR_conduct_standards%202.2%2017122012.pdf 8.