The incremental value of the contribution of a biostatistician to the reporting quality in health research-A retrospective, single center, observational cohort study

Background The reporting quality in medical research has recently been critically discussed. While reporting guidelines intend to maximize the value from funded research, and initiatives such as the EQUATOR network have been introduced to advance high quality reporting, the uptake of the guidelines by researchers could be improved. The aim of this study was to assess the contribution of a biostatistician to the reporting and methodological quality of health research, and to identify methodological knowledge gaps. Methods In a retrospective, single center, observational cohort study, two groups of publications were compared. The group of exposed publications had an academic biostatistician on the author list, whereas the group of non-exposed publications did not include a biostatistician of the evaluated group. Rating of reporting quality was done in blinded fashion and in duplicate. The primary outcome was a sum score based on six dimensions, ranging between 0 (worst) and 11 (best). The study protocol was reviewed and approved as a registered report. Results There were 131 publications in the exposed group published between 2017 and 2018. Of these, 95 were either RCTs, observational, or prediction / prognostic studies. Corresponding matches in the group of non-exposed publications were identified in a reproducible manner. Comparison of reporting quality overall revealed a 1.60 (95%CI from 0.92 to 2.28, p <0.0001) units higher reporting quality for exposed publications. A subgroup analysis within study types showed higher reporting quality across all three study types. Conclusion Our study is the first to report an association of a higher reporting quality and methodological strength in health research publications with a biostatistician on the author list. The higher reporting quality persisted through subgroups of study types and dimensions. Methodological knowledge gaps were identified for prediction / prognostic studies, and for reporting on statistical methods in general and missing values, specifically.