Statistical analysis and reporting: common errors found during peer review and how to avoid them

Worthy, Gillian

doi:10.4414/smw.2015.14076

Cited by 8 publications

(15 citation statements)

References 9 publications

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“…This point is crucial to detect treatment effects. [ 19 21 ] If no sample size calculation was used the study size must be justified, for example, all available patients in two centers were included and a sample size calculation was not relevant. Although the method of randomization/allocation to intervention was not clearly stated in 3.2% of all articles which represented all searched RCT articles.…”

Section: Discussionmentioning

confidence: 99%

“…Clear statistics should be reported, either through labels in the table or as a footnote. [ 21 ] Reporting P value only without test statistics in at least one table was in 53.3% of the articles. These results were in agreement with the study by Hassan et al ., 2015.…”

Section: Discussionmentioning

confidence: 99%

“…Nonparametric tests (e.g., Mann–Whitney U/Wilcoxn rank sum, Kruskal–Wallis H, Wilcoxon signed rank, and Friedman's tests) are suitable in comparison of continuous data not normally distributed expressed in medians and (interquartile range) or mean ranks. [ 20 21 ]…”

Section: Methodsmentioning

confidence: 99%

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Statistical methods and errors in family medicine articles between 2010 and 2014-Suez Canal University, Egypt: A cross-sectional study

Nour-Eldein

2016

J Family Med Prim Care

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Background:With limited statistical knowledge of most physicians it is not uncommon to find statistical errors in research articles.Objectives:To determine the statistical methods and to assess the statistical errors in family medicine (FM) research articles that were published between 2010 and 2014.Methods:This was a cross-sectional study. All 66 FM research articles that were published over 5 years by FM authors with affiliation to Suez Canal University were screened by the researcher between May and August 2015. Types and frequencies of statistical methods were reviewed in all 66 FM articles. All 60 articles with identified inferential statistics were examined for statistical errors and deficiencies. A comprehensive 58-item checklist based on statistical guidelines was used to evaluate the statistical quality of FM articles.Results:Inferential methods were recorded in 62/66 (93.9%) of FM articles. Advanced analyses were used in 29/66 (43.9%). Contingency tables 38/66 (57.6%), regression (logistic, linear) 26/66 (39.4%), and t-test 17/66 (25.8%) were the most commonly used inferential tests. Within 60 FM articles with identified inferential statistics, no prior sample size 19/60 (31.7%), application of wrong statistical tests 17/60 (28.3%), incomplete documentation of statistics 59/60 (98.3%), reporting P value without test statistics 32/60 (53.3%), no reporting confidence interval with effect size measures 12/60 (20.0%), use of mean (standard deviation) to describe ordinal/nonnormal data 8/60 (13.3%), and errors related to interpretation were mainly for conclusions without support by the study data 5/60 (8.3%).Conclusion:Inferential statistics were used in the majority of FM articles. Data analysis and reporting statistics are areas for improvement in FM research articles.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

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Statistical methods and errors in family medicine articles between 2010 and 2014-Suez Canal University, Egypt: A cross-sectional study

Nour-Eldein

2016

J Family Med Prim Care

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“…Of particular concern to colloquium members is improved training in statistical analytical methodology, since misapplication of statistics is common in the scientific literature (Ioannidis 2005;Strasak et al 2007;Worthy 2015;Weissgerber et al 2016;Chavalarias et al 2016). Participants also agreed that the topic of data sharing and compliance with data sharing policies be included in the training of all scientists.…”

Section: Recommendationmentioning

confidence: 99%

Promoting Responsible Scientific Research

Davies¹,

Edwards²

2015

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Concerns around the topic of reproducibility, or rather lack thereof, have exploded onto the public and scientific scenes in recent years. A quick search on "reproducibility" in PubMed reveals that numerous editorials have been published in the scientific literature on the subject, especially within the past five years. Growing concern over a perceived crisis in reproducibility led to a 2014 gathering of scientific and publishing experts at a workshop convened by the National Institutes of Health (NIH), Science, and the Nature Publishing Group. The resultant Principles and Guidelines in Reporting Preclinical Research were widely disseminated and provided recommendations to journals with the aim of increasing transparency and reproducibility in scientific publishing. In addition to endorsing these principles and guidelines, the American Society for Microbiology (ASM) examined its role in addressing these issues within the sphere of the microbial sciences and turned to the American Academy of Microbiology (Academy) for expertise and leadership on the topic of reproducibility in microbial research. The Academy represents members fully invested in the highest standards of rigor and reproducibility in experimental design, interpretation, and reporting. A steering committee appointed prior to the event developed the questions that guided discussion during the colloquium, hosted on 14 and 15 October 2015 at the Washington, DC, headquarters of the ASM. Invited participants included biomedical researchers, university science faculty, editors of several peer-reviewed scientific journals, and ASM personnel involved in the society's extensive publishing efforts. In December 2014, ASM's then President, Tim Donohue, and the society's editors in chief had suggested the colloquium topic while attending the annual ASM Journals Editors in Chief meeting. Colloquium participants considered issues related to reproducibility, the ethical conduct of scientific research, and good practices. The ASM and the Academy sought suggestions on, for example, how publication standards can be upheld globally, procedures that can be implemented in the laboratory to promote ethical practices, and the appropriate consequences for data mishandling. Representing various scientific disciplines, attendees divided into working groups to discuss, periodically reconvening for plenary sessions to report and review the group comments. This report summarizes the plenary discussions during the two-day colloquium.

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“…We wish our discussion will contribute the CDEs of our readers. In the analysis and report of research, relevant guidelines (88,89) and advice (90) should be considered. It should be born in mind that scientists are not immune to biased analysis process (hypothesis myopia, cherry-picked, etc.)…”

Section: Scientific Bases Of Precise Analysismentioning

confidence: 99%

Precision medicine in myasthenia graves: begin from the data precision

Hong

Xie

et al. 2016

Ann. Transl. Med.

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Myasthenia gravis (MG) is a prototypic autoimmune disease with overt clinical and immunological heterogeneity. The data of MG is far from individually precise now, partially due to the rarity and heterogeneity of this disease. In this review, we provide the basic insights of MG data precision, including onset age, presenting symptoms, generalization, thymus status, pathogenic autoantibodies, muscle involvement, severity and response to treatment based on references and our previous studies. Subgroups and quantitative traits of MG are discussed in the sense of data precision. The role of disease registries and scientific bases of precise analysis are also discussed to ensure better collection and analysis of MG data.

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Statistical analysis and reporting: common errors found during peer review and how to avoid them

Cited by 8 publications

References 9 publications

Statistical methods and errors in family medicine articles between 2010 and 2014-Suez Canal University, Egypt: A cross-sectional study

Statistical methods and errors in family medicine articles between 2010 and 2014-Suez Canal University, Egypt: A cross-sectional study

Promoting Responsible Scientific Research

Precision medicine in myasthenia graves: begin from the data precision

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