Reporting data analysis methods in high-impact respiratory journals

Nieminen, Pentti; Toljamo, Tuula; Vähänikkilä, Hannu

doi:10.1183/23120541.00140-2017

Cited by 5 publications

(7 citation statements)

References 24 publications

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“…The availability of statistical software packages has greatly facilitated extensive data analysis and increased the quantity and complexity of using statistical methods. Our findings are in line with previous findings from other medical sub-fields, where clinical researchers have not approved data mining and computationally intensive machine learning methods [25,34,43]. So far, machine learning algorithms that have been developed for prediction purposes have not been useful tools in clinical medicine when the purpose is to understand factors that affect developing diseases or estimate the effectiveness of new treatment methods [44].…”

Section: Discussionsupporting

confidence: 89%

“…Over 33% of the evaluated articles analyzed data that included at least 300 study subjects in 2017. This proportion is still low when compared to articles published in visible psychiatric (63%) or respiratory (50%) journals [25,34]. The previously reported alarming phenomenon of decreasing sample sizes in dentistry has hopefully changed to a trend of greater precision and power [8].…”

Section: Discussionmentioning

confidence: 95%

“…It seems strange that these widespread reporting problems continue for such an important issue. Focusing on clearly stated data analysis methods helps both the investigators to write an understandable and compact report and the readers to evaluate the findings [34]. Nevertheless, considerable research is published without co-authors with adequate training in data analysis methods, and the papers just copy the insufficient descriptions from previous studies.…”

Section: Discussionmentioning

confidence: 99%

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

Nieminen

Vähänikkilä

2020

Publications

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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.

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Section: Discussionsupporting

confidence: 89%

Section: Discussionmentioning

confidence: 95%

Section: Discussionmentioning

confidence: 99%

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

Nieminen

Vähänikkilä

2020

Publications

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“…Adequate evaluation of statistical reporting and data presentation during the submission stage improves the quality of biomedical articles and journals [ 9 , 10 , 11 , 12 ]. Previous studies have shown that statistical information is reported more detailly, comprehensively, and usefully in eminent medical journals [ 13 , 14 , 15 ]. This is consistent with their detailed guidelines for authors, as well as a more strict review process, including extensive statistical reviewing [ 16 ].…”

Section: Introductionmentioning

confidence: 99%

The Quality of Statistical Reporting and Data Presentation in Predatory Dental Journals Was Lower Than in Non-Predatory Journals

Nieminen

Uribe

2021

Entropy

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Proper peer review and quality of published articles are often regarded as signs of reliable scientific journals. The aim of this study was to compare whether the quality of statistical reporting and data presentation differs among articles published in ‘predatory dental journals’ and in other dental journals. We evaluated 50 articles published in ‘predatory open access (OA) journals’ and 100 clinical trials published in legitimate dental journals between 2019 and 2020. The quality of statistical reporting and data presentation of each paper was assessed on a scale from 0 (poor) to 10 (high). The mean (SD) quality score of the statistical reporting and data presentation was 2.5 (1.4) for the predatory OA journals, 4.8 (1.8) for the legitimate OA journals, and 5.6 (1.8) for the more visible dental journals. The mean values differed significantly (p < 0.001). The quality of statistical reporting of clinical studies published in predatory journals was found to be lower than in open access and highly cited journals. This difference in quality is a wake-up call to consume study results critically. Poor statistical reporting indicates wider general lower quality in publications where the authors and journals are less likely to be critiqued by peer review.

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“…The principle of SAMPL is that “authors should describe statistical methods with sufficient detail to enable readers in the professional domain to access raw data to verify the results of the report”. In 2017, Pentti Nieminen et al made the SIMA (Statistical Intensity of Medical Articles) tool and assessed the statistical intensity in the high impact factor respiratory journal’s articles, and found that approximately one third of the respiratory papers provided incomplete description of their statistical reports (7, 8). Even though the SAMPL guidelines and SIMA broaden the standards for the scrutiny of statistical methods, there is still a void in requiring or assessing the reporting of statistical methods in observational studies.…”

Section: Introductionmentioning

confidence: 99%

Assessing the statistical reporting quality in high-impact factor urology journals

Dai

et al. 2020

Preprint

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11Backgrounds Observational studies plays an important role in urology studies, But few studies have 12 paid attention to the statistical reporting quality of observational studies. The purpose of this study 13 was to investigate the frequency and evaluate the reporting quality of statistical methods of the 14 published observational studies in urology. 15 Methods The five urology journals were selected according to the 5-year impact factor. A systematic 16 literature search was performed in PubMed for relevant articles. The quality of statistical reporting was 17 assessed according to assessment criteria. 18 Results A total of 193 articles were included in this study. The mean statistical reporting score of 19 included articles was 0.42 (SD=0.15), accounting for 42% of total score. The items that must be 20 reported with a reporting rate more than 50% were: alpha level (n=122, 65.2%), confidence intervals 21 (n=134, 69.4%), name of statistical package (n=158, 84.5%) and exact P-values (n=161, 86.1%). The 22 items with a reporting rate less than 50% were: outliers (n=2, 1.0%) and sample size (n=13, 6.7%). For 23 multivariable regression models (liner, logistic and Cox), variables coding (n=27, 40.7%), validation 24 analysis of assumptions (n=58, 40.3%), interaction test (n=43, 30.0%), collinearity diagnostics (n=5, 25 3.5%) and goodness of fit test (n=6, 5.9%) were reported. Number of authors more than 7(OR=2.06, 26 95%CI=1.04-4.08) and participation of statistician or epidemiologist (OR=1.73, 95%CI=1.18-3.39) 27 were associated with the superior reporting quality. 28 Conclusion The statistical reporting quality of published observational studies in 5 high-impact factor 29 urological journals was alarming. We encourage researchers to collaborate with statistician or 30 epidemiologist. The authors, reviewers and editors should increase their knowledge of statistical 31 methods, especially new and complex methods. 32

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Reporting data analysis methods in high-impact respiratory journals

Cited by 5 publications

References 24 publications

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

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

The Quality of Statistical Reporting and Data Presentation in Predatory Dental Journals Was Lower Than in Non-Predatory Journals

Assessing the statistical reporting quality in high-impact factor urology journals

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