Skip to main content
Dryad logo

Adaptive, randomized, non-inferiority trial to evaluate the efficacy of monoclonal antibodies in outpatients with mild or moderate COVID-19

Citation

Tacconelli, Evelina (2022), Adaptive, randomized, non-inferiority trial to evaluate the efficacy of monoclonal antibodies in outpatients with mild or moderate COVID-19 , Dryad, Dataset, https://doi.org/10.5061/dryad.tdz08kq2w

Abstract

The dataset is based on the results of a trial called MANTICO (Clinical trial number NCT05205759). The study is a non-inferiority randomised controlled trial comparing the clinical efficacy of bamlanivimab/etesevimab, casirivimab/imdevimab, and sotrovimab in outpatients aged 50 or older with early COVID-19. The primary outcome was COVID-19 progression (hospitalisation, need of supplemental oxygen therapy, or death through day 14).  

Methods

Data were collected during the implementation of a randomised clinical trial (Clinical trial number NCT05205759). Study data were collected and managed using REDCap electronic data capture tool hosted at the Verona University Hospital. A web-based eCRF (electronic Case Report Form) was developed and patient randomisation was centrally managed and monitored by the coordinating centre. Data monitoring was also carried out with STATA and the variables for the analysis were manipulated and created using the same software.   In line with ethics board approval and informed consent signed by study participants, some variables, namely age, gender, height, weight, smoking habit, were removed from the original dataset used for the analysis. Pseudonymization was also used in order to guarantee patients' anonymity. In addition, the dataset record order was randomised so the resulting dataset is a file very similar in terms of length, fields and content to the original version, except for row order which is now completely random, and the record ID variable deleted.

Usage Notes

STATA v.17 with an ad hoc codebook

Funding

Agenzia Italiana del Farmaco, Ministero della Salute

Horizon 2020, Award: 101016167