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Published on: February 23, 2019
Statistical inference in abstracts of three influential clinical pharmacology journals analyzed using a text-mining
Marjan Amiri1,2, Markus Deckert3, Martin C Michel4,5
1Institute of Medical Informatics, Biometry and Epidemiology, University Hospital Essen, University of Duisburg-Essen, Essen, North Rhine-Westphalia, Germany.
Statistical inference prevalence in clinical pharmacology journals remained stable from 1976-2016. Null-hypothesis significance testing was most common, with authors favoring it over estimation despite guidelines.
Area of Science:
- Pharmacology
- Biostatistics
- Scientific Publishing
Background:
- Statistical inference is crucial in clinical pharmacology research.
- Trends in statistical reporting may impact research interpretation.
- Understanding these trends is vital for scientific communication.
Purpose of the Study:
- To analyze the prevalence and trends of statistical inference in influential clinical pharmacology journals.
- To evaluate the performance of a computer-based algorithm for identifying statistical inference.
Main Methods:
- A computer-based algorithm was used to analyze abstracts from 1976-2016.
- Manual review of 300 articles assessed algorithm accuracy for abstracts and full text.
- Prevalence of statistical inference subtypes was quantified.
Main Results:
- 59% of abstracts contained statistical inference, with similar prevalence in 1976 and 2016.
- Null-hypothesis significance testing was the most common statistical inference method.
- The algorithm demonstrated high accuracy for abstracts but lower predictive value for full text.
Conclusions:
- Authors predominantly used null-hypothesis significance testing over estimation, despite existing guidelines.
- Improving authors' statistical knowledge and journal guidelines are recommended for better statistical reporting.
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