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

Emilyane de Oliveira Santana Amaral () and Sergio Roberto Peres Line ()
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Emilyane de Oliveira Santana Amaral: University of Campinas
Sergio Roberto Peres Line: University of Campinas

Scientometrics, 2021, vol. 126, issue 11, No 15, 9133-9145

Abstract: 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.

Keywords: Data Analysis; Methodology; Statistical Analysis; Effect Size; 62–11 (research data for problems pertaining to statistics); 62P10 (applications of statistics to biology and medical sciences); 92B15 (general biostatistics) (search for similar items in EconPapers)
JEL-codes: C12 C18 I10 (search for similar items in EconPapers)
Date: 2021
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DOI: 10.1007/s11192-021-04150-3

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