DOCSLIB.ORG

On the Reproducibility of Meta-Analyses.Docx

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
On the Reproducibility of Meta-Analyses.Docx On the Reproducibility of Meta-Analyses 1 1 RUNNING HEAD: ON THE REPRODUCIBILITY OF META-ANALYSES 2 3 4 On the Reproducibility of Meta-Analyses: Six Practical Recommendations 5 6 Daniël Lakens 7 Eindhoven University of Technology 8 Joe Hilgard 9 University of Missouri - Columbia 10 Janneke Staaks 11 University of Amsterdam 12 13 In Press, BMC Psychology 14 15 Word Count: 6371 16 17 Keywords: Meta-Analysis; Reproducibility; Open Science; Reporting Guidelines 18 19 Author Note: Correspondence can be addressed to Daniël Lakens, Human Technology 20 Interaction Group, IPO 1.33, PO Box 513, 5600MB Eindhoven, The Netherlands. E-mail: 21 [email protected]. We would like to thank Bianca Kramer for feedback on a previous draft 22 of this manuscript. 23 24 Competing Interests: The authors declared that they had no competing interest with respect 25 to their authorship or the publication of this article. 26 27 Author Contributions: All authors contributed writing this manuscript. DL drafted the 28 manuscript, DL, JH, and JS revised the manuscript. All authors read and approved the final 29 manuscript. 30 31 Email Addresses: D. Lakens: [email protected] 32 Joe Hilgard: [email protected] 33 Janneke Staaks: [email protected] On the Reproducibility of Meta-Analyses 2 34 Abstract 35 Background 36 Meta-analyses play an important role in cumulative science by combining information 37 across multiple studies and attempting to provide effect size estimates corrected for 38 publication bias. Research on the reproducibility of meta-analyses reveals that errors are 39 common, and the percentage of effect size calculations that cannot be reproduced is much 40 higher than is desirable. Furthermore, the flexibility in inclusion criteria when performing a 41 meta-analysis, combined with the many conflicting conclusions drawn by meta-analyses of 42 the same set of studies performed by different researchers, has led some people to doubt 43 whether meta-analyses can provide objective conclusions. 44 Discussion 45 The present article highlights the need to improve the reproducibility of meta-analyses 46 to facilitate the identification of errors, allow researchers to examine the impact of subjective 47 choices such as inclusion criteria, and update the meta-analysis after several years. 48 Reproducibility can be improved by applying standardized reporting guidelines and sharing 49 all meta-analytic data underlying the meta-analysis, including quotes from articles to specify 50 how effect sizes were calculated. Pre-registration of the research protocol (which can be peer- 51 reviewed using novel ‘registered report’ formats) can be used to distinguish a-priori analysis 52 plans from data-driven choices, and reduce the amount of criticism after the results are 53 known. 54 Summary 55 The recommendations put forward in this article aim to improve the reproducibility of 56 meta-analyses. In addition, they have the benefit of “future-proofing” meta-analyses by 57 allowing the shared data to be re-analyzed as new theoretical viewpoints emerge or as novel 58 statistical techniques are developed. Adoption of these practices will lead to increased 59 credibility of meta-analytic conclusions, and facilitate cumulative scientific knowledge. On the Reproducibility of Meta-Analyses 3 60 On the Reproducibility of Meta-Analyses: Six Practical Recommendations 61 Any single study is just a data-point in a future meta-analysis. Meta-analyses play an 62 important role in cumulative science by combining information across multiple studies. 63 Especially when sample sizes are small, effect size estimates can vary substantially from one 64 study to the next. Statistical inferences are even more complicated due to publication bias. 65 Because statistically significant studies are more likely to be published, it is a challenge to 66 draw quantifiable conclusions about the presence or absence of effects based on the published 67 literature. Meta-analytic statistical tools can provide a partial solution to these problems by 68 drawing conclusions over multiple independent studies and calculating effect size estimates 69 that attempt to take publication bias into account. Although meta-analyses do not provide 70 definitive conclusions, they are typically interpreted as state-of-the-art empirical knowledge 71 about a specific effect or research area. Large-scale meta-analyses often accumulate a 72 massive number of citations and influence future research and theory development. It is 73 therefore essential that published meta-analyses are of the highest possible quality. 74 To guarantee this quality, both pre- and post-publication peer review are important 75 tools. Peers can check the quality of meta-analyses only when the meta-analytic effect size 76 calculations are reproducible. Unfortunately, this is rarely the case. Gøtzsche, Hróbjartsson, 77 Marić, and Tendal (2007) examined whether standardized mean differences in meta-analyses 78 were correctly calculated and reproducible for 27 meta-analyses. In 10 (37%) of these 79 studies, results could not be reproduced within a predefined difference of the effect size 80 estimate of 0.1. Among these ten meta-analyses, one that had yielded a significant meta- 81 analytic effect was no longer significant, three without significant results yielded significant 82 results when reproduced, and one meta-analysis was retracted. While quality control is 83 important, there are other benefits to reproducible meta-analyses. When meta-analyses are 84 well-documented and transparent, it is easier to re-analyze published meta-analyses using 85 different inclusion criteria. Such re-analyses can yield important insights that differ from the 86 conclusions in the initial meta-analysis. For example, Derry, Derry, McQuay, and Moore 87 (2006) found that even though many systematic reviews had concluded acupuncture was 88 beneficial, their meta-analysis using stricter inclusion criteria (such as randomized, double 89 blind trials) revealed strong evidence that acupuncture had no benefit. 90 Beyond re-analyses, making meta-analytic data publically available facilitates 91 continuously cumulating meta-analyses that update effect size estimates as new data are 92 collected (e.g., Braver, Thoemmes, & Rosenthal, 2014) or as new theoretical viewpoints or 93 statistical techniques are developed. Meta-analyses need to be updated regularly to prevent On the Reproducibility of Meta-Analyses 4 94 outdated scientific conclusions from influencing public policy or real-life applications. 95 Cochrane reviews are required to be updated every two years, or else the lack of an update 96 needs to be justified (The Cochrane Collaboration, 2011). Similarly, authors of Campbell 97 reviews are obliged to plan for an update within five years after publication (The Campbell 98 Collaboration, 2014a). If the data underlying meta-analyses were openly accessible and 99 reproducible, such updates may become more common in psychology than they are now, 100 which would facilitate cumulative knowledge. If meta-analytic efforts are to stand the test of 101 time, it is essential that the extracted meta-analytic data can be easily updated and reanalyzed 102 by researchers in the future. 103 The open accessibility of meta-analytic data may also make these analyses more 104 objective and convincing. A lack of openness about the data and choices for inclusion criteria 105 underlying meta-analyses has been raised as one of the problems in resolving debates 106 following the publication of meta-analyses (e.g., Bushman, Rothstein, & Anderson, 2010). As 107 Ferguson (2014a) argues, “meta-analyses have failed in replacing narrative reviews as more 108 objective”. Researchers on different sides of a scientific argument often reach different 109 conclusions in their meta-analyses of the same literature (see Goodyear-Smith, van Driel, 110 Arroll, & Del Mar, 2012, for detailed discussion of different meta-analyses on depression 111 screening with opposite recommendations). We believe this situation can be improved 112 substantially by implementing six straightforward recommendations that increase the 113 openness and reproducibility of meta-analyses. We have summarized these recommendations 114 in Table 1, and will expand upon each in the remainder of this article. 115 116 Table 1: Six Practical Recommendations to Improve the Reproducibility of 117 Meta-Analyses 118 1. Facilitate Cumulative Science by Future-Proofing Meta-Analyses: 119 Disclose all meta-analytic data (effect sizes, sample sizes for each condition, test 120 statistics and degrees of freedom, means, standard deviations, and correlations 121 between dependent observations) for each data point. Quote relevant text from studies 122 that describe the meta-analytic data to prevent confusion, such as when one effect size 123 is selected from a large number of tests reported in a study. When analyzing 124 subgroups, include quotes from the original study that underlie this classification, and 125 specify any subjective decisions. On the Reproducibility of Meta-Analyses 5 126 2. Facilitate quality control: Specify which effect size calculations are used and 127 which assumptions are made for missing data (e.g., assuming equal sample sizes in 128 each condition, imputed values for unreported effect sizes), if necessary for each 129 effect size extracted from the literature. Specify who extracted and coded the data, 130 knowing it is preferable that two researchers independently extract effect sizes from 131 the literature. 132 3. Adhere to
Load more

Recommended publications
  • Reproducibility, Replicability, and Generalization in the Social, Behavioral, and Economic Sciences
    REPRODUCIBILITY, REPLICABILITY, AND GENERALIZATION IN THE SOCIAL, BEHAVIORAL, AND ECONOMIC SCIENCES Report of the Subcommittee on Replicability in Science of the SBE Advisory Committee to the National Science Foundation 13 May 2015 Presentation at SBE AC Spring Meeting by K. Bollen. Report of the Subcommittee on Replicability in Science Advisory Committee to the NSF SBE Directorate Subcommittee Members: Kenneth Bollen (University of North Carolina at Chapel Hill, Cochair) John T. Cacioppo (University of Chicago, Cochair) Robert M. Kaplan (Agency for Healthcare Research and Quality) Jon A. Krosnick (Stanford University) James L. Olds (George Mason University) Staff Assistance Heather Dean (National Science Foundation) TOPICS I. Background II. Subcommittee Report III. Definitions IV. Recommendations V. Final Comments Background • Spring, 2013 o NSF SBE Advisory Committee establishes subcommittee on how SBE can promote robust research practices • Summer & Fall 2013 o Subcommittee proposal for workshop on “Robust Research in the Social, Behavioral, and Economic Sciences.” • February 20-21, 2014 o Workshop convened o Participants drawn from variety of universities, funding agencies, scientific associations, and journals o Cover broad range of issues from extent and cause of problems to possible solutions o Details are in the appendix of the circulated report • Post-workshop period o Document & digest workshop content o Discuss and propose recommendations o Complete report Subcommittee Report • DEFINITIONS o No consensus in science on the meanings
    [Show full text]
  • Metabolic Sensor Governing Bacterial Virulence in Staphylococcus Aureus
    Metabolic sensor governing bacterial virulence in PNAS PLUS Staphylococcus aureus Yue Dinga, Xing Liub, Feifei Chena, Hongxia Dia, Bin Xua, Lu Zhouc, Xin Dengd,e, Min Wuf, Cai-Guang Yangb,1, and Lefu Lana,1 aDepartment of Molecular Pharmacology and bChinese Academy of Sciences Key Laboratory of Receptor Research, Shanghai Institute of Materia Medica, Chinese Academy of Sciences, Shanghai 201203, China; cDepartment of Medicinal Chemistry, School of Pharmacy, Fudan University, Shanghai 201203, China; dDepartment of Chemistry and eInstitute for Biophysical Dynamics, The University of Chicago, Chicago, IL 60637; and fDepartment of Basic Sciences University of North Dakota School of Medicine and Health Sciences, Grand Forks, ND 58203 Edited by Richard P. Novick, New York University School of Medicine, New York, NY, and approved October 14, 2014 (received for review June 13, 2014) An effective metabolism is essential to all living organisms, in- To survive and replicate efficiently in the host, S. aureus has cluding the important human pathogen Staphylococcus aureus.To developed exquisite mechanisms for scavenging nutrients and establish successful infection, S. aureus must scavenge nutrients adjusting its metabolism to maintain growth while also coping with and coordinate its metabolism for proliferation. Meanwhile, it also stress (6, 11). On the other hand, S. aureus produces a wide array must produce an array of virulence factors to interfere with host of virulence factors to evade host immune defenses and to derive defenses. However, the ways in which S. aureus ties its metabolic nutrition either parasitically or destructively from the host during state to its virulence regulation remain largely unknown. Here we infections (6).
    [Show full text]
  • Principles of Validation of Diagnostic Assays for Infectious Diseases1
    PRINCIPLES OF VALIDATION OF DIAGNOSTIC ASSAYS FOR INFECTIOUS DISEASES1 R. H. JACOBSON, New York State College of Veterinary Medicine, Cornell University, Ithaca, New York, USA Abstract PRINCIPLES OF VALIDATION OF DIAGNOSTIC ASSAYS FOR INFECTIOUS DISEASES. Assay validation requires a series of inter-related processes. Assay validation is an experimental process: reagents and protocols are optimized by experimentation to detect the analyte with accuracy and precision. Assay validation is a relative process: its diagnostic sensitivity and diagnostic specificity are calculated relative to test results obtained from reference animal populations of known infection/exposure status. Assay validation is a conditional process: classification of animals in the target population as infected or uninfected is conditional upon how well the reference animal population used to validate the assay represents the target population; accurate predictions of the infection status of animals from test results (PV+ and PV−) are conditional upon the estimated prevalence of disease/infection in the target population. Assay validation is an incremental process: confidence in the validity of an assay increases over time when use confirms that it is robust as demonstrated by accurate and precise results; the assay may also achieve increasing levels of validity as it is upgraded and extended by adding reference populations of known infection status. Assay validation is a continuous process: the assay remains valid only insofar as it continues to provide accurate and precise results as proven through statistical verification. Therefore, the work required for validation of diagnostic assays for infectious diseases does not end with a time-limited series of experiments based on a few reference samples rather, to assure valid test results from an assay requires constant vigilance and maintenance of the assay, along with reassessment of its performance characteristics for each unique population of animals to which it is applied.
    [Show full text]
  • New NIH Guidelines to Improve Experimental Rigor and Reproducibility
    New NIH guidelines to improve Experimental Rigor and Reproducibility Oswald Steward Professor, Department of Anatomy & Neurobiology Senior Associate Dean for Research University of California School of Medicine What I’ll tell you 1) Background: Increasing reports of failures to replicate 2) Previous actions by NIH to enhance rigor 3) New requirements for grants submitted after January 2016 4) What NIH will be looking for in new required sections 5) Some suggested best practices to meet new standards for scientific rigor • The Problem: Numerous recent reports of findings that can’t be replicated. Failure to replicate key results from preclinical cancer studies: 1) Prinz et al. (2011) Believe it or not: How much can we rely on published data on potential drug targets? Nature Reviews Drug Discovery. Inconsistencies were found in 2/3 studies 2) Begley and Ellis (2012) Raise standards for preclinical cancer research. Nature. Only 6/53 “landmark” papers replicated 3) Begley (2013) Six red flags for suspect work. Nature. -Were experiments performed blinded? -Were basic experiments repeated? -Were all the results presented? -Were there positive and negative controls? -Were reagents validated? -Were statistical tests appropriate? • In ALS field, Scott et al., (2008) re-assessed 70 drugs that had been reported to increase lifespan in an animal model of ALS (SOD1-G93A knockout mice). • 221 separate studies were carried out over a five year period that involved over 18,000 mice. • Some drugs were already in clinical trials. • Outcome: There was no statistically significant positive effect for any of the 70 compounds tested. • Conclusion: Previously published effects were measurements of noise in the distribution of survival means as opposed to an actual drug effect.
    [Show full text]
  • Studies of Staphylococcal Infections. I. Virulence of Staphy- Lococci and Characteristics of Infections in Embryonated Eggs * WILLIAM R
    Journal of Clinical Investigation Vol. 43, No. 11, 1964 Studies of Staphylococcal Infections. I. Virulence of Staphy- lococci and Characteristics of Infections in Embryonated Eggs * WILLIAM R. MCCABE t (From the Research Laboratory, West Side Veterans Administration Hospital, and the Department of Medicine, Research and Educational Hospitals, University of Illinois College of Medicine, Chicago, Ill.) Many of the determinants of the pathogenesis niques still require relatively large numbers of and course of staphylococcal infections remain staphylococci to produce infection (19). Fatal imprecisely defined (1, 2) despite their increas- systemic infections have been equally difficult to ing importance (3-10). Experimental infections produce in animals and have necessitated the in- in suitable laboratory animals have been of con- jection of 107 to 109 bacteria (20-23). A few siderable assistance in clarifying the role of host strains of staphylococci have been found that are defense mechanisms and specific bacterial virulence capable of producing lethal systemic infections factors with a variety of other infectious agents. with inocula of from 102 to 103 bacteria (24) and A sensitive experimental model would be of value have excited considerable interest (25-27). The in defining the importance of these factors in virulence of these strains apparently results from staphylococcal infections, but both humans and an unusual antigenic variation (27, 28) which, the usual laboratory animals are relatively re- despite its interest, is of doubtful significance in sistant. Extremely large numbers of staphylo- human staphylococcal infection, since such strains cocci are required to produce either local or sys- have been isolated only rarely from clinical in- temic infections experimentally.
    [Show full text]
  • 10 Things to Know About Reproducibility and Replicability
    10 Things to Know About Reproducibility and Replicability One of the pathways by which the scientific community confirms the validity of a new scientific discovery is by repeating the research that produced it. When a scientific effort fails to independently confirm the computations or results of a previous study, some argue that such an observed inconsistency can be an important precursor to new discovery while others fear it may be a symptom of a lack of rigor in science. When a newly reported scientific study has far-reaching implications for science or a major, potential impact on the public, the question of its reliability takes on heightened importance. 1 The terms reproducibility and replicability take on 6 Not all studies can be replicated. While scientists are a range of meanings in contemporary usage. The report able to test for replicability of most studies, it is impossible to do distinguishes and defines the terms as follows: Reproducibility so for studies of ephemeral phenomena. means obtaining consistent results using the same input data, computational steps, methods, and conditions of analysis; it is synonymous with computational reproducibility. Replicability 7 One type of scientific research tool, statistical means obtaining consistent results across studies aimed at inference, has an outsized role in replicability discussions answering the same scientific question, each of which has due to the frequent misuse of statistics and the use of a obtained its own data. p-value threshold for determining “statistical significance.” Biases in published research can occur due to the excess reliance on and misunderstanding of statistical significance. 2 Reproducibility and replicability matter.
    [Show full text]
  • Lab Services Accuracy and Reproducibility
    Accuracy and Reproducibility ABOUT The Laboratory Services Committee of the American Thyroid Association® (ATA) conducted a survey of ATA® members to identify areas of member interest for education in pathology and laboratory medicine. In response to the results of the survey, the Lab Service Committee developed a series of educational materials to share with the ATA® membership. The topics below were ranked as high educational priorities amongst the membership. FIGURE 1A: FIGURE 1B: Single perfectly accurate result. Three inaccurate results that ACCURACY AND PRECISION are accurate on average WHAT ARE THE INDICATORS OF TEST RELIABILITY? The most commonly used descriptors of analytical performance for laboratories are accuracy and precision. Accuracy is the degree to which the test results approximate the value that would be obtained by a reference method, and precision is the reproducibility of test results among measurements, days, and operating conditions. The metrics of analytical performance are distinct from the metrics of clinical performance, which are focused on sensitivity, specificity, and predictive values. FIGURE 1C: Three perfectly precise but ACCURACY (TRUENESS) inaccurate results. A test method is said to be accurate when the values it produces are close to the values that are obtained from a reference method. Thus, PRECISION (REPEATABILITY) the concept of accuracy is highly dependent on the correct choice of The description of accuracy above demonstrates why this metric alone a reference method as all judgments of accuracy treat the reference is insufficient to characterize how a test performs. Three arrows shown method as truth; deviations from the reference method are therefore in (Figure 1B) are all very inaccurate, but they average to be accurate.
    [Show full text]
  • A Novel High Throughput, Parallel Infection Assay for Determining The
    viruses Article A Novel High Throughput, Parallel Infection Assay for Determining the Replication Capacities of 346 Primary HIV-1 Isolates of the Zurich Primary HIV-1 Infection Study in Primary Cells Audrey E. Rindler 1,2,3 , Herbert Kuster 1,2 , Kathrin Neumann 1,2 , Christine Leemann 1,2, Dominique L. Braun 1,2 , Karin J. Metzner 1,2,* and Huldrych F. Günthard 1,2,* 1 Division of Infectious Diseases and Hospital Epidemiology, University Hospital Zürich, 8091 Zürich, Switzerland; [email protected] (A.E.R.); [email protected] (H.K.); [email protected] (K.N.); [email protected] (C.L.); [email protected] (D.L.B.) 2 Institute of Medical Virology, University of Zürich, 8057 Zürich, Switzerland 3 Life Sciences Graduate School, University of Zürich, 8057 Zürich, Switzerland * Correspondence: [email protected] (K.J.M.); [email protected] (H.F.G.) Abstract: HIV-1 replication capacity is an important characteristic to understand the replication competence of single variants or virus populations. It can further aid in the understanding of HIV-1 pathogenicity, disease progression, and drug resistance mutations. To effectively study RC, many assays have been established. However, there is still demand for a high throughput replication capacity assay using primary cells which is robust and reproducible. In this study, we established Citation: Rindler, A.E.; Kuster, H.; such an assay and validated it using 346 primary HIV-1 isolates from patients enrolled in the Neumann, K.; Leemann, C.; Braun, Zurich Primary HIV Infection study (ZPHI) and two control viruses, HIV-1 JR-CSF and HIV-1 D.L.; Metzner, K.J.; Günthard, H.F.
    [Show full text]
  • 1 Maximizing the Reproducibility of Your Research Open
    1 Maximizing the Reproducibility of Your Research Open Science Collaboration1 Open Science Collaboration (in press). Maximizing the reproducibility of your research. In S. O. Lilienfeld & I. D. Waldman (Eds.), Psychological Science Under Scrutiny: Recent Challenges and Proposed Solutions. New York, NY: Wiley. Authors’ Note: Preparation of this chapter was supported by the Center for Open Science and by a Veni Grant (016.145.049) awarded to Hans IJzerman. Correspondence can be addressed to Brian Nosek, [email protected]. 1 Alexander A. Aarts, Nuenen, The Netherlands; Frank A. Bosco, Virginia Commonwealth University; Katherine S. Button, University of Bristol; Joshua Carp, Center for Open Science; Susann Fiedler, Max Planck Institut for Research on Collective Goods; James G. Field, Virginia Commonwealth University; Roger Giner-Sorolla, University of Kent; Hans IJzerman, Tilburg University; Melissa Lewis, Center for Open Science; Marcus Munafò, University of Bristol; Brian A. Nosek, University of Virginia; Jason M. Prenoveau, Loyola University Maryland; Jeffrey R. Spies, Center for Open Science 2 Commentators in this book and elsewhere describe evidence that modal scientific practices in design, analysis, and reporting are interfering with the credibility and veracity of the published literature (Begley & Ellis, 2012; Ioannidis, 2005; Miguel et al., 2014; Simmons, Nelson, & Simonsohn, 2011). The reproducibility of published findings is unknown (Open Science Collaboration, 2012a), but concern that is lower than desirable is widespread -
    [Show full text]
  • RCR Rigor and Reproducibility
    Responsible Conduct of Research Workshop Series, 2017-2018 Rigor and Reproducibility -­‐-­‐ March 1, 2018-­‐-­‐ Rigour or rigor (see spelling differences) describes a condition of stiffness or strictness. Rigour frequently refers to a process of adhering absolutely to certain constraints, or the practice of maintaining strict consistency with certain predefined parameters. Webster’s DicBonary accessed 2016 December Rigor Allende, J.. Rigor – The essence of scientific work. Electronic Journal of Biotechnolo gy, North America, 724 07 2012. Have you talked about rigor in your research group? 1. Yes 2. No. 3. Maybe Reproducibility is the ability of an entire experiment or study to be duplicated, either by the same researcher or by someone else working independently. Reproducing an experiment is called replicating it. Reproducibility is one of the main principles of the scientific method. But a new paper in Science Translational Medicine argues that the current movement to replicate results is crippled by a lack of agreement about the very nature of the word “replication” and its synonyms. hDps://www.statnews.com/2016/06/01/reproducibility-­‐ hDps://en.wikipedia.org/wiki/Reproducibility science/ Have you talked about reproducibility in your research group? 1. Yes 2. No. 3. Maybe I followed the m•thods In your paper to the last Well .... When did you do the experiment? On a Fr iday? detail..• This experl ment doesn't world Oooh.M that's why! It never works on Fridays! Anll 20t'> S !otTWTfS /' )A'J<~"' .,/r' Y.a'>... ..,.,,,.'SJ! ,P . .. ll ll a. l> i. l' ll 1' ID Rigor, Transparency and Reproducibility Carrie Dykes, PhD Research Engagement Specialist ' ' I C~ Al &'l'IJ A ' 'SLATIONAL sca1'cE ..;{u.
    [Show full text]
  • Companion Guidelines on Replication and Reproducibility in Education
    Companion Guidelines on Replication & Reproducibility in Education Research A Supplement to the Common Guidelines for Education Research and Development A Report from The National Science Foundation and The Institute of Education Sciences, U.S. Department of Education Nov 28, 2018 Introduction The Institute of Education Sciences (IES) and the National Science Foundation (NSF) jointly issued the Common Guidelines for Education Research and Development in 2013 to describe “shared understandings of the roles of various types of ‘genres’ of research in generating evidence about strategies and interventions for increasing student learning” (IES and NSF, 2013: 7). In the intervening period, the education research community and federal policymakers have been increasingly attentive to the role of, and factors that promote and inhibit, replication and reproducibility of research. In order to build a coherent body of work to inform evidence-based decision making, there is a need to increase the visibility and value of reproducibility and replication studies among education research stakeholders. The purpose of this companion to the Common Guidelines is to highlight the importance of these studies and provide crossagency guidance on the steps investigators are encouraged to take to promote corroboration, ensure the integrity of education research, and extend the evidence base. The companion begins with a brief overview of the central role of replication in the advancement of science, including definitions of key terminology for the purpose of establishing a common understanding of the concepts. The companion also addresses the challenges and implications of planning and conducting reproducibility and replication studies within education. Background and terminology Efforts to reproduce and replicate research findings are central to the accumulation of scientific knowledge that helps inform evidence-based decision making and policies.
    [Show full text]
  • Reproducibility of Clinical Research in Critical Care: a Scoping Review Daniel J
    Niven et al. BMC Medicine (2018) 16:26 https://doi.org/10.1186/s12916-018-1018-6 RESEARCH ARTICLE Open Access Reproducibility of clinical research in critical care: a scoping review Daniel J. Niven1*, T. Jared McCormick2, Sharon E. Straus3, Brenda R. Hemmelgarn4, Lianne Jeffs5, Tavish R. M. Barnes1 and Henry T. Stelfox1 Abstract Background: The ability to reproduce experiments is a defining principle of science. Reproducibility of clinical research has received relatively little scientific attention. However, it is important as it may inform clinical practice, research agendas, and the design of future studies. Methods: We used scoping review methods to examine reproducibility within a cohort of randomized trials examining clinical critical care research and published in the top general medical and critical care journals. To identify relevant clinical practices, we searched the New England Journal of Medicine, The Lancet, and JAMA for randomized trials published up to April 2016. To identify a comprehensive set of studies for these practices, included articles informed secondary searches within other high-impact medical and specialty journals. We included late-phase randomized controlled trials examining therapeutic clinical practices in adults admitted to general medical-surgical or specialty intensive care units (ICUs). Included articles were classified using a reproducibility framework. An original study was the first to evaluate a clinical practice. A reproduction attempt re-evaluated that practice in a new set of participants. Results: Overall, 158 practices were examined in 275 included articles. A reproduction attempt was identified for 66 practices (42%, 95% CI 33–50%). Original studies reported larger effectsthanreproductionattempts(primary endpoint, risk difference 16.0%, 95% CI 11.6–20.5% vs.
    [Show full text]