IDEAS home Printed from https://ideas.repec.org/a/spr/scient/v126y2021i11d10.1007_s11192-021-04150-3.html
   My bibliography  Save this article

Current use of effect size or confidence interval analyses in clinical and biomedical research

Author

Listed:
  • Emilyane de Oliveira Santana Amaral

    (University of Campinas)

  • Sergio Roberto Peres Line

    (University of Campinas)

Abstract

The isolated use of the statistical hypothesis testing for two group comparison has limitations, and its combination with effect size or confidence interval analysis as complementary statistical tests is recommended. In the present work, we estimate the use of these complementary statistical tests (i.e. effect size or confidence interval) in recently published in research articles in clinical and biomedical areas. Methods: The ProQuest database was used to search published studies in academic journals between 2019 and 2020. The analysis was carried out using terms that represent five areas of clinical and biomedical research: “brain”, “liver”, “heart”, “dental”, and “covid-19”. A total of 119,558 published articles were retrieved. Results: The relative use of complementary statistical tests in clinical and biomedical publications was low. The highest frequency usage of complementary statistical tests was among articles that also used statistical hypothesis testing for two-sample comparison. Publications with the term “covid-19” showed the lowest usage rate of complementary statistical tests when all article were analyzed but presented the highest rate among articles that used hypothesis testing. Conclusion: The low use of effect size or confidence interval in two-sample comparison suggests that coordinate measures should be taken in order to increase the use of this analysis in clinical and biomedical research. Their use should be emphasized in statistical disciplines for college and graduate students, become a routine procedure in research laboratories, and recommended by reviewers and editors of scientific journals.

Suggested Citation

  • Emilyane de Oliveira Santana Amaral & Sergio Roberto Peres Line, 2021. "Current use of effect size or confidence interval analyses in clinical and biomedical research," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9133-9145, November.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:11:d:10.1007_s11192-021-04150-3
    DOI: 10.1007/s11192-021-04150-3
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11192-021-04150-3
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s11192-021-04150-3?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Fidler, Fiona & Geoff, Cumming & Mark, Burgman & Neil, Thomason, 2004. "Statistical reform in medicine, psychology and ecology," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 33(5), pages 615-630, November.
    2. Regina Nuzzo, 2014. "Scientific method: Statistical errors," Nature, Nature, vol. 506(7487), pages 150-152, February.
    3. Steven N. Goodman, 2019. "Why is Getting Rid of P-Values So Hard? Musings on Science and Statistics," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 26-30, March.
    4. Colin B. Begg & Jesse A. Berlin, 1988. "Publication Bias: A Problem in Interpreting Medical Data," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 151(3), pages 419-445, May.
    5. Ioana Alina Cristea & John P A Ioannidis, 2018. "P values in display items are ubiquitous and almost invariably significant: A survey of top science journals," PLOS ONE, Public Library of Science, vol. 13(5), pages 1-15, May.
    6. Ronald L. Wasserstein & Allen L. Schirm & Nicole A. Lazar, 2019. "Moving to a World Beyond “p," The American Statistician, Taylor & Francis Journals, vol. 73(S1), pages 1-19, March.
    7. Andrew Gelman & John Carlin, 2017. "Some Natural Solutions to the -Value Communication Problem—and Why They Won’t Work," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(519), pages 899-901, July.
    8. Daniele Fanelli, 2012. "Negative results are disappearing from most disciplines and countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 90(3), pages 891-904, March.
    9. Parisa Soltani & Romeo Patini, 2020. "Retracted COVID-19 articles: a side-effect of the hot race to publication," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 819-822, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Megan L Head & Luke Holman & Rob Lanfear & Andrew T Kahn & Michael D Jennions, 2015. "The Extent and Consequences of P-Hacking in Science," PLOS Biology, Public Library of Science, vol. 13(3), pages 1-15, March.
    2. Jakub Bijak, 2019. "Editorial: P-values, theory, replicability, and rigour," Demographic Research, Max Planck Institute for Demographic Research, Rostock, Germany, vol. 41(32), pages 949-952.
    3. David J. Hand, 2022. "Trustworthiness of statistical inference," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 185(1), pages 329-347, January.
    4. Gunter, Ulrich & Önder, Irem & Smeral, Egon, 2019. "Scientific value of econometric tourism demand studies," Annals of Tourism Research, Elsevier, vol. 78(C), pages 1-1.
    5. Eleni Verykouki & Christos T. Nakas, 2023. "Adaptations on the Use of p -Values for Statistical Inference: An Interpretation of Messages from Recent Public Discussions," Stats, MDPI, vol. 6(2), pages 1-13, April.
    6. Jyotirmoy Sarkar, 2018. "Will P†Value Triumph over Abuses and Attacks?," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 7(4), pages 66-71, July.
    7. Parisa Soltani & Kimia Baghaei & Kioumars Tavakoli Tafti & Gianrico Spagnuolo, 2021. "Science Mapping Analysis of COVID-19 Articles Published in Dental Journals," IJERPH, MDPI, vol. 18(4), pages 1-8, February.
    8. Stanley, T.D. & Doucouliagos, Chris & Jarrell, Stephen B., 2008. "Meta-regression analysis as the socio-economics of economics research," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 37(1), pages 276-292, February.
    9. Stanley, T. D. & Doucouliagos, Chris, 2019. "Practical Significance, Meta-Analysis and the Credibility of Economics," IZA Discussion Papers 12458, Institute of Labor Economics (IZA).
    10. Karin Langenkamp & Bodo Rödel & Kerstin Taufenbach & Meike Weiland, 2018. "Open Access in Vocational Education and Training Research," Publications, MDPI, vol. 6(3), pages 1-12, July.
    11. Chatfield, Chris, 1995. "Positive or negative?," International Journal of Forecasting, Elsevier, vol. 11(4), pages 501-502, December.
    12. Gruener, Sven, 2019. "An empirical study on Internet-based false news stories: experiences, problem awareness, and responsibilities," SocArXiv xbez9, Center for Open Science.
    13. Christopher Snyder & Ran Zhuo, 2018. "Sniff Tests as a Screen in the Publication Process: Throwing out the Wheat with the Chaff," NBER Working Papers 25058, National Bureau of Economic Research, Inc.
    14. Mattia Filomena & Matteo Picchio, 2023. "Retirement and health outcomes in a meta‐analytical framework," Journal of Economic Surveys, Wiley Blackwell, vol. 37(4), pages 1120-1155, September.
    15. Auspurg Katrin & Hinz Thomas, 2011. "What Fuels Publication Bias?: Theoretical and Empirical Analyses of Risk Factors Using the Caliper Test," Journal of Economics and Statistics (Jahrbuecher fuer Nationaloekonomie und Statistik), De Gruyter, vol. 231(5-6), pages 636-660, October.
    16. Augusteijn, Hilde Elisabeth Maria & van Aert, Robbie Cornelis Maria & van Assen, Marcel A. L. M., 2021. "Posterior Probabilities of Effect Sizes and Heterogeneity in Meta-Analysis: An Intuitive Approach of Dealing with Publication Bias," OSF Preprints avkgj, Center for Open Science.
    17. Gunnarsson, Björn Rafn & vanden Broucke, Seppe & Baesens, Bart & Óskarsdóttir, María & Lemahieu, Wilfried, 2021. "Deep learning for credit scoring: Do or don’t?," European Journal of Operational Research, Elsevier, vol. 295(1), pages 292-305.
    18. Maria-Lavinia FLOREA & Anca BORZA, 2019. "Individual Strategies For Achieving Work-Life Balance €“ A Case Study On Romanian Workers," Proceedings of the INTERNATIONAL MANAGEMENT CONFERENCE, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 13(1), pages 845-855, November.
    19. Hirschauer, Norbert & Grüner, Sven & Mußhoff, Oliver & Becker, Claudia & Jantsch, Antje, 2020. "Can p-values be meaningfully interpreted without random sampling?," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 14, pages 71-91.
    20. Arthur Matsuo Yamashita Rios de Sousa & Hideki Takayasu & Misako Takayasu, 2017. "Detection of statistical asymmetries in non-stationary sign time series: Analysis of foreign exchange data," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-18, May.

    More about this item

    Keywords

    Data Analysis; Methodology; Statistical Analysis; Effect Size;
    All these keywords.

    JEL classification:

    • I10 - Health, Education, and Welfare - - Health - - - General
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:scient:v:126:y:2021:i:11:d:10.1007_s11192-021-04150-3. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.