Clinical trial data transparency should match data access to scientific purpose: a tiered framework for reproducibility and reuse
Abstract
Background: Clinical trial data sharing is increasingly endorsed by journals, funders, regulators, and research organisations. Nevertheless, access to individual participant data (IPD) remains inconsistent and may not be available when trial results are published. Access timelines can reflect several distinct components, including scientific and governance review, contractual and legal procedures, data preparation, de-identification, transfer, and subsequent harmonisation. When original data are unavailable, researchers may reconstruct approximate participant-level event-time data from published Kaplan-Meier curves. Trinquart and Stockler recently proposed that trialists instead release minimal analysable time-to-event data at publication. Their proposal raises a broader question: what kinds of data and documentation are required for different forms of reproducibility and scientific reuse?
Main text: We argue that clinical trial data transparency should be considered in relation to the scientific claims and research activities that a particular data object can support. We propose a three-tier framework for data intentionally released or made accessible by trialists or data custodians. Tier 1 comprises original publication-time reduced datasets and supports endpoint-level numerical reproducibility and analyses that are functions of the released observations. Tier 2 comprises reproducibility packages containing analysis-ready data, code, derivation logic, and documentation sufficient to reproduce the reported analytic process. Tier 3 provides governed access to broader de-identified IPD for substantively new analyses and reassessment of operational and analytic decisions. Reconstructed pseudo-IPD are considered separately because they are generated externally from publications rather than shared from the trial database. Privacy protection, governance, and processing environment are cross-cutting dimensions and do not map uniquely to a particular tier. They also apply differently to each tier.
Conclusions: Publication-time reduced datasets can provide meaningful and timely transparency when broader IPD are not yet accessible. Their value should be recognised without confusing endpoint-level numerical reproducibility with reproduction of the complete analytic process or broader scientific reuse. We propose that publication-time reduced datasets should form a practical floor for timely transparency, while reproducibility packages and governed access to broader IPD provide progressively richer opportunities for reproducing, interrogating, and extending clinical trial analyses.
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BibTeX
@article{mansmann2026clinical,
title = {{Clinical trial data transparency should match data access to scientific purpose: a tiered framework for reproducibility and reuse}},
author = {Ulrich Mansmann and Ka Hin Tai and Giulia Varvarà and Florian Naudet},
journal = {BMC Medicine},
year = {2026},
month = oct,
doi = {10.1186/s12916-026-05349-y},
url = {https://link.springer.com/article/10.1186/s12916-026-05349-y},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Clinical trial data transparency should match data access to scientific purpose: a tiered framework for reproducibility and reuse
AU - Ulrich Mansmann
AU - Ka Hin Tai
AU - Giulia Varvarà
AU - Florian Naudet
PY - 2026
DA - 2026/10/22
JO - BMC Medicine
DO - 10.1186/s12916-026-05349-y
UR - https://link.springer.com/article/10.1186/s12916-026-05349-y
AB - Background: Clinical trial data sharing is increasingly endorsed by journals, funders, regulators, and research organisations. Nevertheless, access to individual participant data (IPD) remains inconsistent and may not be available when trial results are published. Access timelines can reflect several distinct components, including scientific and governance review, contractual and legal procedures, data preparation, de-identification, transfer, and subsequent harmonisation. When original data are unavailable, researchers may reconstruct approximate participant-level event-time data from published Kaplan-Meier curves. Trinquart and Stockler recently proposed that trialists instead release minimal analysable time-to-event data at publication. Their proposal raises a broader question: what kinds of data and documentation are required for different forms of reproducibility and scientific reuse? Main text: We argue that clinical trial data transparency should be considered in relation to the scientific claims and research activities that a particular data object can support. We propose a three-tier framework for data intentionally released or made accessible by trialists or data custodians. Tier 1 comprises original publication-time reduced datasets and supports endpoint-level numerical reproducibility and analyses that are functions of the released observations. Tier 2 comprises reproducibility packages containing analysis-ready data, code, derivation logic, and documentation sufficient to reproduce the reported analytic process. Tier 3 provides governed access to broader de-identified IPD for substantively new analyses and reassessment of operational and analytic decisions. Reconstructed pseudo-IPD are considered separately because they are generated externally from publications rather than shared from the trial database. Privacy protection, governance, and processing environment are cross-cutting dimensions and do not map uniquely to a particular tier. They also apply differently to each tier. Conclusions: Publication-time reduced datasets can provide meaningful and timely transparency when broader IPD are not yet accessible. Their value should be recognised without confusing endpoint-level numerical reproducibility with reproduction of the complete analytic process or broader scientific reuse. We propose that publication-time reduced datasets should form a practical floor for timely transparency, while reproducibility packages and governed access to broader IPD provide progressively richer opportunities for reproducing, interrogating, and extending clinical trial analyses.
SN - 1741-7015
ER -