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Statistical inference in abstracts of 3 influential clinical pharmacology journals analysed using a text‐mining algorithm
Author(s) -
Amiri Marjan,
Deckert Markus,
Michel Martin C.,
Poole Charles,
Stang Andreas
Publication year - 2021
Publication title -
british journal of clinical pharmacology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.216
H-Index - 146
eISSN - 1365-2125
pISSN - 0306-5251
DOI - 10.1111/bcp.14836
Subject(s) - statistical inference , inference , null hypothesis , statistical hypothesis testing , statistical significance , statistical model , computer science , predictive inference , fiducial inference , statistical theory , statistical analysis , frequentist inference , statistics , artificial intelligence , machine learning , mathematics , bayesian inference , bayesian probability
Aims To describe the trend in the prevalence of statistical inference in 3 influential clinical pharmacology journals. Methods We applied a computer‐based algorithm to abstracts of 3 clinical pharmacology journals published in 1976–2016 to identify statistical inference and its subtypes. Furthermore, we manually reviewed a random sample of 300 articles to access algorithm's performance in finding statistical inference in abstracts and as a screening tool for presence and absence of statistical inference in full text. Results The algorithm identified 59% (13 375/22 516 [mid‐ P 95% confidence interval {CI}, 59–60%]) article abstracts with statistical inference. The percentage of abstracts with statistical inference was similar in 1976 and 2016, 48% (179/377 [mid‐ P 95%CI, 42–52%]) vs . 49% (386/791 [mid‐ P 95%CI, 45–52%]). Statistical reporting pattern varied among journals. Among abstracts containing any statistical inference in the publications from 1976 to 2016, null‐hypothesis significance testing was the most prevalent reported statistical inference. The algorithm had high sensitivity, specificity, positive predictive value and negative predictive value for finding statistical inferences in abstract. While positive predictive value for predicting the statistical inference in full text (including abstract, text, tables and figures) was high, negative predictive value was low. Conclusion Despite journals’ editorials and statistical associations' guidelines, most authors focused on testing rather than estimation. In the future, better statistical reporting might be ensured by improving the statistical knowledge of authors and an addition of statistical guides to journals' instruction to authors to the extent that editors would like their statistical inference preferences to be incorporated into submitted manuscripts.