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Original Article| Volume 61, ISSUE 3, P277-281, March 2008

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A survey of abstracts of high-impact clinical journals indicated most statistical methods presented are summary statistics

  • Nathan Taback
    Correspondence
    Corresponding author. Centre for Research on Inner City Health, The Keenan Research Centre in the Li Ka Shing Knowledge Institute of St. Michael's Hospital, 30 Bond Street, Toronto, Ontario, Canada M5B 1W8. Tel.: 416-864-6060 ext. 2891; fax: 416-864-5485.
    Affiliations
    Centre for Research on Inner City Health, The Keenan Research Centre in the Li Ka Shing Knowledge Institute of St. Michael's Hospital, 30 Bond Street, Toronto, Ontario, Canada M5B 1W8

    Department of Public Health Sciences, University of Toronto, 30 Bond Street, Toronto, Ontario, Canada M5B 1W8
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  • Monika K. Krzyzanowska
    Affiliations
    Department of Medical Oncology and Hematology, Princess Margaret Hospital, 610 University Avenue, Suite 5-227, Toronto, Ontario, Canada M5G 2M9

    Department of Medicine, University of Toronto, 610 University Avenue, Suite 5-227, Toronto, Ontario, Canada M5G 2M9
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Published:September 17, 2007DOI:https://doi.org/10.1016/j.jclinepi.2007.05.003

      Abstract

      Objectives

      To assess what statistical methods are commonly used in high-impact clinical research and how they are presented in abstracts of articles published in high-impact medical journals.

      Study Design and Setting

      A cross-sectional survey of abstracts of original articles published in July 2003 in four high-impact medical journals was conducted. The primary outcome was the distribution of statistical methods used in study results presented in the abstract of articles.

      Results

      Seventy articles met inclusion criteria. One hundred twenty-five unique statistical method presentations were analyzed. Sixty-eight percent of statistical methods used summary statistics, and 27.2% used regression analysis. When summary statistics were used, clinical evidence was presented with a P-value or confidence interval (CI) in 51.8% of statistical methods compared to 72.5% when summary statistics were not used (P=0.0282). Clinical evidence was presented verbally in 7.1% of statistical methods when summary statistics were used and in 20.0% when summary statistics were not used (P=0.0323).

      Conclusions

      Summary statistics are the most frequently used statistical method to generate high-impact clinical evidence presented in the abstract of a medical article. Evidence described by summary statistics is significantly associated with less frequent reporting of a P-value or CI, and less frequent verbal presentations.

      Keywords

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      References

        • Hoffrage U.
        • Lindsey S.
        • Hertwig R.
        • Gigerenzer G.
        Medicine. Communicating statistical information.
        Science. 2000; 290: 2261-2262
        • Hoffrage U.
        • Gigerenzer G.
        Using natural frequencies to improve diagnostic inferences.
        Acad Med. 1998; 73: 538-540
        • Freedman L.S.
        The next 10 years of biostatistics.
        Stat Methods Med Res. 2000; 9: 25-30
        • Gehan E.A.
        Biostatistics in the new millennium: a consulting statistician's perspective.
        Stat Methods Med Res. 2000; 9: 3-16
        • van Houwelingen H.C.
        The future of biostatistics: expecting the unexpected.
        Stat Med. 1997; 16: 2773-2784
        • Tukey J.W.
        The future of data analysis.
        Ann Math Stat. 1962; 33: 1-67
        • Efron B.
        Bayesians, frequentists, and scientists.
        J Am Stat Assoc. 2005; 100: 1-5
        • Emerson J.D.
        • Colditz G.A.
        Use of statistical analysis in the New England Journal of Medicine.
        N Engl J Med. 1983; 309: 709-713
        • Altman D.G.
        • Goodman S.N.
        Transfer of technology from statistical journals to the biomedical literature. Past trends and future predictions.
        JAMA. 1994; 272: 129-132
      1. Thomson Scientific. Journal Citation Reports help menu. 2006.

      2. Ann Intern Med. 2004; 140 ([Editorial]): 480-481
        • Groves T.
        • Abbasi K.
        Screening research papers by reading abstracts.
        BMJ. 2004; 329: 470-471