Discipline IV · INSECAB Analytical Framework
An analysis that stands up to any audit, year after year. Reproducibility is not a final step: it is part of the workflow from day one. At INSECAB it is taught as a basic professional skill, not as an advanced option.
The difficulty of reproducing published results is a recognised problem in biomedicine. Various studies have documented low reproduction rates, particularly in preclinical research.
The causes are multiple: from a lack of documentation of the analytical process to the absence of shared code and detailed protocols.
A well-documented, auditable analysis reaches peer review in far better shape — and survives a change of team.
All of INSECAB's projects are carried out to these standards. Students learn reproducibility by applying the same methodological practices the team uses in its projects — without accessing our partners' real data, for reasons of confidentiality.
The INSECAB technology stack
Tools and practices
Applying the principles of reproducible science with R and Python: code documentation, project organisation, package management and the generation of reproducible reports.
Systematic review of the analytical pipeline to detect errors, inconsistencies and deviations from the registered protocol. Aligned with ICH E9 standards and good clinical research practice.
INSECAB training
The training passes on the team's experience, methodology and judgement — worked through on analogous cases and public data in order to preserve the confidentiality of ongoing projects.
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