Quantitative evaluation of multiplicity in epidemiology and public health research

Kenneth J. Ottenbacher

    Research output: Contribution to journalArticlepeer-review

    69 Scopus citations

    Abstract

    Epidemiologic and public health researchers frequently include several dependent variables, repeated assessments, or subgroup analyses in their investigations. These factors result in multiple tests of statistical significance and may produce type 1 experimental errors. This study examined the type 1 error rate in a sample of public health and epidemiologic research. A total of 173 articles chosen at random from 1996 issues of the American Journal of Public Health and the American Journal of Epidemiology were examined to determine the incidence of type 1 errors. Three different methods of computing type 1 error rates were used: experiment-wise error rate, error rate per experiment, and percent error rate. The results indicate a type 1 error rate substantially higher than the traditionally assumed level of 5% (p < 0.05). No practical or statistically significant difference was found between type 1 error rates across the two journals. Methods to determine and correct type 1 errors should be reported in epidemiologic and public health research investigations that include multiple statistical tests.

    Original languageEnglish (US)
    Pages (from-to)615-619
    Number of pages5
    JournalAmerican journal of epidemiology
    Volume147
    Issue number7
    DOIs
    StatePublished - Apr 1 1998

    Keywords

    • Bias (epidemiology)
    • Probability
    • Research design
    • Significance tests

    ASJC Scopus subject areas

    • Epidemiology

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