An instrument to assess the statistical intensity of medical research papers

Statistical Reporting in Nursing Research: Addressing a Common Error in Reporting of p Values (p = .000)

PURPOSE

The confidence in a study will be reduced due to the incorrect representation of statistical results. However, it is unknown to what extent p values are incorrectly represented in published nursing journals. The study aims to evaluate the articles in 30 nursing journals in terms of the error in reporting of p values (p = .000).

DESIGN AND METHODS

This was a bibliometric analysis. All papers published in 10 leading nursing journals (between 2015 and 2019), the 10 bottom nursing journals (2019), and 10 selected key nursing journals (2019) indexed in the Science Citation Index Journal Citation Reports were reviewed to detect errors in reporting of p values (p = .000).

RESULTS

A total of 3,788 papers were reviewed. Notably, it was found that 93.3% (28/30) of the nursing journals contained incorrect representation of p values (p = .000). The reporting rate of these journals ranges from 0% to 57.1%, with an overall rate of 12.8% (486/3,788). In addition, the rate of incorrect representation of p values (p = .000) showed no statistically significant difference between different publication years (Χ² = 4.976, p = .290). However, the rate of reporting was different between study types, journals, and regions (p = .007, p = .020, and p < .001, respectively).

CONCLUSIONS

The incorrect representation of p values is common in nursing journals.

CLINICAL RELEVANCE

We recommend that both publishers and researchers be responsible for preventing statistical errors in manuscripts. Furthermore, various kinds of statistical training methods should be adopted to ensure that nurses and journal reviewers have enough statistical literacy.

Use of Data Analysis Methods in Dental Publications: Is There Evidence of a Methodological Change?

Objectives: To evaluate how data analysis methods in dental studies have changed in recent years. Methods: A total of 400 articles published in 2010 and 2017 in five dental journals, Journal of Dental Research, Caries Research, Community Dentistry and Oral Epidemiology, Journal of Dentistry, and Acta Odontologica Scandinavica, were analyzed. The study characteristics and the reporting of data analysis techniques were systematically identified. Results: The statistical intensity of the dental journals did not change from 2010 to 2017. Dental researchers did not adopt the data mining, machine learning, or Bayesian approaches advocated in the computer-oriented methodological literature. The determination of statistical significance was the most generally used method for conducting research in both 2010 and 2017. Observational study designs were more common in 2017. Insufficient and incomplete descriptions of statistical methods were still a serious problem. Conclusion: The stabilization of statistical intensity in the literature suggests that papers applying highly computationally complex data analysis methods have not meaningfully contributed to dental research or clinical care. Greater rigor is required in reporting the methods in dental research articles, given the current pervasiveness of failure to describe the basic techniques used.

Applications of Raman spectroscopy in the development of cell therapies: state of the art and future perspectives

This comprehensive review article discusses current and future perspectives of Raman spectroscopy-based analyses of cell therapy processes and products.

Reporting of data analysis methods in psychiatric journals: Trends from 1996 to 2018

OBJECTIVES

The article aims to evaluate how study designs and data analysis methods in psychiatric studies have changed over the last 22 years.

METHODS

This study involved a total of 320 papers published in 1996 and 2018 in the American Journal of Psychiatry, Acta Psychiatrica Scandinavica, British Journal of Psychiatry, and JAMA Psychiatry. We manually reviewed the articles to determine the way in which they reported the study characteristics and the methods applied in data analysis.

RESULTS

The statistical intensity in psychiatric journals has changed over the past 20 years. Traditional methods of testing statistical significance were widely used both in 1996 and in 2018. In 2018, there was an increase in reporting more complex methods, such as multivariable regression models, multilevel modelling, and intracluster correlation methods. However, computationally complex data mining or machine learning procedures were not adopted by psychiatric researchers.

CONCLUSION

The increase in statistical intensity in the literature suggests that readers of prominent psychiatric journals must possess a substantial level of statistical expertise if they wish to critically evaluate the findings published in these journals. It is essential to include an awareness of this substantial change in data analysis methods in psychiatric undergraduate and postgraduate education.

Statistical errors in articles published in radiology journals

PURPOSE

We aimed to evaluate articles in radiology journals indexed in the Science Citation Index or Science Citation Index Expanded in terms of statistical errors. By this means, we aim to contribute to the production of high quality scientific publications.

METHODS

In this study, a total of 157 articles published in 2016-2017 in 20 radiology journals were reviewed randomly. Selected articles were evaluated for statistical errors regarding P values and statistical tests, and for errors in terminology and other errors related to interpretation. In addition, in order to examine whether the error rates of the articles published in the radiology journals differed according to the impact factor, the statistical errors were compared according to the impact factors of the journals.

RESULTS

Of the 157 articles published in radiology journals, 10 had no statistical errors, while 147 had at least one statistical error. The most frequently encountered error was "errors in summarizing data" with a rate of 66%. This was followed by "incorrect representation of P values" with a rate of 42%. The least frequently encountered error was "statistical symbol errors" with a rate of 3%. There was no statistically significant difference according to impact factors.

CONCLUSION

In conclusion, radiology journals, as do journals in different fields, include articles containing statistical errors. Even when the quality of the journal increases, there is no difference in these statistical error rates. In order to prevent statistical errors in manuscripts, there are responsibilities for both the researchers who conduct scientific studies and the editors who publish these studies in their journals. Researchers should have a basic statistical knowledge, and the editor must submit all manuscripts for a statistical review.

Reporting data analysis methods in high-impact respiratory journals

Data analysis methods play an important role in respiratory research. We evaluated the application and complexity of data analytical methods in high-impact respiratory journals and compared the statistical reporting in these respiratory articles with reports published in other eminent medical journals. This study involved a total of 160 papers published in 2015 in the European Respiratory Journal, American Journal of Respiratory and Critical Care Medicine, Chest and Thorax, and 680 papers published between 2007-2015 in other medical journals including the Lancet and New England Journal of Medicine. We manually reviewed the articles to determine the way in which they reported the methods applied in data analysis. The statistical intensity in the respiratory journals was equal to that in eminent medical journals. Traditional ways of testing statistical significance were widely used in respiratory articles. Statistical procedures were not always described in sufficient detail, and the prominent respiratory journals did not display different profiles with respect to their statistical content. Readers of the prominent respiratory journals need to possess a substantial level of statistical expertise if they wish to critically evaluate the design, methodology, data analysis and interpretation of the findings published in these journals.