A vision for the European journal of clinical investigation: note from the new editors

Requirements for Study Registration and Adherence to Reporting Guidelines in Surgery Journals: A Cross-Sectional Study

BACKGROUND

Reporting guidelines and study registration can minimize bias and improve the reporting quality of biomedical research, but may not be fully utilized. The objective of this study was to investigate the policies of surgery journals as for reporting guidelines and study registration and explore associated journal characteristic variables.

METHODS

Study samples were obtained from the Expanded Science Citation Index of the 2018 Journal Citation Reports (surgery category). The online guides for authors were browsed to identify which journals endorsed reporting guidelines and study registration. The predictors related to the endorsement were explored by using Chi-square test and multivariate logistic regression analysis, respectively.

RESULTS

One hundred and eighty-eight surgery journals were included in our study. One hundred and sixty-three journals (86.7%) endorsed reporting guidelines and 103 journals (54.8%) endorsed study registration. About reporting guidelines, ICMJE (International Committee of Medical Journal Editors) recommendations were the most frequently endorsed (n = 155, 82.4%) by journals, followed by CONSORT (Consolidated Standards of Reporting Trials) statement (n = 94, 50.0%). About study registration, randomized controlled trial registration was endorsed by 101 (53.7%) journals, whereas the systematic review registration was endorsed by only 9 journals (4.8%). The results of multivariate logistic regression analysis revealed that not North America, higher JCR (Journal Citation Reports) rank journals were more likely to endorse reporting guidelines and study registration.

CONCLUSIONS

Surgery journals frequently use reporting guidelines, but nearly half of journals did not require study registration. Implementing these two mechanisms can prevent bias, and their adoption should be strengthened by authors, reviewers and journal editors in surgery.

Replicability in psychological research: a reflection

Background: In recent years, psychological science has suffered a crisis of confidence that has been marked by the low rate of replicability demonstrated in collaborative projects that attempted to quantify this problem, evidencing the difficulty in making replications and the existence of a possible excess of false positives published in the scientific literature. Method: This opinion article aimed to review the panorama of the replicability crisis in psychology, as well as its possible causes. Conclusions: It began from the state of the replicability crisis, then some possible causes and their repercussions on the advancement of psychological science were highlighted, discussing various associated issues, such as individual biases on the part of researchers, the lack of incentives to replicability studies and the priority standards that journals would currently have for novel and positive studies. Finally, the existing alternatives to reverse this situation are mentioned, among them the opening to new statistical approaches, the restructuring of incentives, and the development of editorial policies that facilitate the means for replication.

Does the P Value Have a Future in Plant Pathology?

The P value (significance level) is possibly the mostly widely used, and also misused, quantity in data analysis. P has been heavily criticized on philosophical and theoretical grounds, especially from a Bayesian perspective. In contrast, a properly interpreted P has been strongly defended as a measure of evidence against the null hypothesis, H0. We discuss the meaning of P and null-hypothesis statistical testing, and present some key arguments concerning their use. P is the probability of observing data as extreme as, or more extreme than, the data actually observed, conditional on H0 being true. However, P is often mistakenly equated with the posterior probability that H0 is true conditional on the data, which can lead to exaggerated claims about the effect of a treatment, experimental factor or interaction. Fortunately, a lower bound for the posterior probability of H0 can be approximated using P and the prior probability that H0 is true. When one is completely uncertain about the truth of H0 before an experiment (i.e., when the prior probability of H0 is 0.5), the posterior probability of H0 is much higher than P, which means that one needs P values lower than typically accepted for statistical significance (e.g., P = 0.05) for strong evidence against H0. When properly interpreted, we support the continued use of P as one component of a data analysis that emphasizes data visualization and estimation of effect sizes (treatment effects).

Transparent and accurate reporting increases reliability, utility, and impact of your research: reporting guidelines and the EQUATOR Network

Although current electronic methods of scientific publishing offer increased opportunities for publishing all research studies and describing them in sufficient detail, health research literature still suffers from many shortcomings. These shortcomings seriously undermine the value and utility of the literature and waste scarce resources invested in the research. In recent years there have been several positive steps aimed at improving this situation, such as a strengthening of journals' policies on research publication and the wide requirement to register clinical trials.The EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network is an international initiative set up to advance high quality reporting of health research studies; it promotes good reporting practices including the wider implementation of reporting guidelines. EQUATOR provides free online resources http://www.equator-network.org supported by education and training activities and assists in the development of robust reporting guidelines. This paper outlines EQUATOR's goals and activities and offers suggestions for organizations and individuals involved in health research on how to strengthen research reporting.