Statistical analysis
We coded the 22 STROBE items as article section and article sub-section items attributing a dichotomous YES/NO score according to a conservative (all sub-items defined by the STROBE explanatory article were satisfied) and liberal (at least 50% of sub-items were satisfied) criteria. Items without sub-items, i.e. the STROBE items on discussion and funding, were coded the same for the conservative and liberal score. Afterwards, the qualitative nature of STROBE was taken into account describing the 22 dichotomous scores by means of percentages. We merged results regarding multiple STROBE items belonging to the same article section by means of medians. Finally, we performed a multiple correspondence analysis, which is an analogue of principal component analysis applied to contingency tables from categorical data. We applied this technique to investigate the multivariate association between STROBE items and their correlation with study design. The correspondence analyses was performed using the FactoMineR package of the R software. Results are reported using graphical representations as bar charts of frequencies, scatter plot of percentages over publication years and correspondence analysis plot of the first two dimensions.