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      The credibility crisis in research: Can economics tools help?

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      Author(s): 1 ,   2 , 1 , *
      Publication date (Electronic):
      Journal: PLoS Biology
      Publisher: Public Library of Science

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          The issue of nonreplicable evidence has attracted considerable attention across biomedical and other sciences. This concern is accompanied by an increasing interest in reforming research incentives and practices. How to optimally perform these reforms is a scientific problem in itself, and economics has several scientific methods that can help evaluate research reforms. Here, we review these methods and show their potential. Prominent among them are mathematical modeling and laboratory experiments that constitute affordable ways to approximate the effects of policies with wide-ranging implications.

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          A call for transparent reporting to optimize the predictive value of preclinical research.

          Story C Landis, Susan Amara, Khusru Asadullah … (2012)
          The US National Institute of Neurological Disorders and Stroke convened major stakeholders in June 2012 to discuss how to improve the methodological reporting of animal studies in grant applications and publications. The main workshop recommendation is that at a minimum studies should report on sample-size estimation, whether and how animals were randomized, whether investigators were blind to the treatment, and the handling of data. We recognize that achieving a meaningful improvement in the quality of reporting will require a concerted effort by investigators, reviewers, funding agencies and journal editors. Requiring better reporting of animal studies will raise awareness of the importance of rigorous study design to accelerate scientific progress.
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            The Rules of the Game Called Psychological Science.

            Marjan Bakker, Annette van Dijk, Jelte Wicherts (2012)
            If science were a game, a dominant rule would probably be to collect results that are statistically significant. Several reviews of the psychological literature have shown that around 96% of papers involving the use of null hypothesis significance testing report significant outcomes for their main results but that the typical studies are insufficiently powerful for such a track record. We explain this paradox by showing that the use of several small underpowered samples often represents a more efficient research strategy (in terms of finding p < .05) than does the use of one larger (more powerful) sample. Publication bias and the most efficient strategy lead to inflated effects and high rates of false positives, especially when researchers also resorted to questionable research practices, such as adding participants after intermediate testing. We provide simulations that highlight the severity of such biases in meta-analyses. We consider 13 meta-analyses covering 281 primary studies in various fields of psychology and find indications of biases and/or an excess of significant results in seven. These results highlight the need for sufficiently powerful replications and changes in journal policies.
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              Good News and Bad News: Representation Theorems and Applications

              Paul R. Milgrom (1982)
              Article 0 comments Cited 243 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-ta): PLoS Biol
                Journal ID (iso-abbrev): PLoS Biol
                Journal ID (publisher-id): plos
                Journal ID (pmc): plosbiol
                Title: PLoS Biology
                Publisher: Public Library of Science (San Francisco, CA USA )
                ISSN (Print): 1544-9173
                ISSN (Electronic): 1545-7885
                Publication date (Electronic): 26 April 2017
                Publication date Collection: April 2017
                Publication date PMC-release: 26 April 2017
                Volume: 15
                Issue: 4
                Electronic Location Identifier: e2001846
                Affiliations
                [1 ]Economics Department, School of Social Sciences, University of Southampton, Southampton, United Kingdom
                [2 ]Meta-Research Innovation Center at Stanford (METRICS), Stanford University, Stanford, California, United States of America
                Author notes

                The authors have declared that no competing interests exist.

                Article
                Publisher ID: pbio.2001846
                DOI: 10.1371/journal.pbio.2001846
                PMC ID: 5405914
                PubMed ID: 28445470
                SO-VID: c6e89ae0-15bc-4b54-8978-f9b6e750026e
                Copyright © © 2017 Gall et al
                License:

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                Page count
                Figures: 1, Tables: 2, Pages: 13
                Funding
                University of Southampton www.southampton.ac.uk (grant number 512188118 SSF). The work of John Ioannidis is supported by an unrestricted gift from Sue and Bob O'Donnell. METRICS is funded by a grant from the Laura and John Arnold Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
                Categories
                Subject: Perspective
                Subject: Social Sciences
                Subject: Economics
                Subject: Experimental Economics
                Subject: Social Sciences
                Subject: Economics
                Subject: Social Sciences
                Subject: Economics
                Subject: Labor Economics
                Subject: Research and Analysis Methods
                Subject: Research Assessment
                Subject: Peer Review
                Subject: Medicine and Health Sciences
                Subject: Clinical Medicine
                Subject: Clinical Trials
                Subject: Randomized Controlled Trials
                Subject: Medicine and Health Sciences
                Subject: Pharmacology
                Subject: Drug Research and Development
                Subject: Clinical Trials
                Subject: Randomized Controlled Trials
                Subject: Research and Analysis Methods
                Subject: Clinical Trials
                Subject: Randomized Controlled Trials
                Subject: Research and Analysis Methods
                Subject: Simulation and Modeling
                Subject: Mathematical Modeling
                Subject: Biology and Life Sciences
                Subject: Behavior
                Subject: Social Sciences
                Subject: Economics
                Subject: Health Economics
                Subject: Medicine and Health Sciences
                Subject: Health Care
                Subject: Health Economics

                ScienceOpen disciplines: Life sciences
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                ScienceOpen disciplines: Life sciences

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