Work

Streamlining statistical reproducibility: NHLBI ORCHID clinical trial results reproduction

Year: 2022

Type: article

Abstract: Reproducibility in medical research has been a long-standing issue. More recently, the COVID-19 pandemic has publicly underlined this fact as the retraction of several studies reached out to general media audiences. A significant number of these retractions occurred after in-depth scrutiny of the methodology and results by the scientific community. Consequently, these retractions have undermined confidence in the peer-review process, which is not considered sufficiently reliable to generate trust in the published results. This partly stems from opacity in published results, the practical implementation of the statistical analysis often remaining undisclosed. We present a workflow that uses a combination of informatics tools to foster statistical reproducibility: an open-source programming language, Jupyter Notebook, cloud-based data repository, and an application programming interface can streamline an analysis and help to kick-start new analyses. We illustrate this principle by (1) reproducing the results of the ORCHID clinical trial, which evaluated the efficacy of hydroxychloroquine in COVID-19 patients, and (2) expanding on the analyses conducted in the original trial by investigating the association of premedication with biological laboratory results. Such workflows will be encouraged for future publications from National Heart, Lung, and Blood Institute-funded studies.

Source: JAMIA Open

DOI: 10.1093/jamiaopen/ooac001

Authors: Arnaud Serret-Larmande, Jonathan R Kaltman, Paul Avillach

Institutions: Boston Children's Hospital, Harvard University, National Institutes of Health, National Heart, Lung, and Blood Institute

Language: English

Topic: Scientific Computing and Data Management

Subfield: Information Systems and Management

Field: Decision Sciences

Domain: Social Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,332