ArticleTrials2026
Supporting reanalysis and reuse of clinical trial data: a case study.
Article in Trials, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
Abstract
backgroundReproducing published findings from clinical trials is a critical component of scientific transparency, yet it remains a challenging and under-practiced task. Despite increasing emphasis on reproducibility and data reuse in research policies, only few real-world examples exist where several teams have reproduced complex analyses using clinical trial data. In this case study, the aim was to reproduce the key findings of a high-impact clinical trial on rectal cancer treatment using shared trial data.
methodWe organized a multi-team datathon, where each team was provided with the same dataset and supporting material, and was tasked to reproduce the results of the CAO/ARO/AIO-04 trial, with optional additional analyses. We contacted the original investigators for access and reuse of the data, as well as information on the clinical and scientific aspects of the study.
resultsFive teams used R or Python to reproduce the statistical results, and the corresponding scripts can be found on Gitlab. The key findings on disease-free survival (DFS) were consistently reproduced by most teams, reinforcing confidence in the main trial conclusions. Result robustness was investigated using different analytical software or statistical models. Some challenges were encountered because supplementary material of the original study was not easily found. Minor reporting issues were also identified in the reproduced paper.
conclusionsReproduction of a major oncology clinical trial confirmed the reliability of its main conclusions. Divergences highlighted reporting gaps-such as incomplete protocols and broken links-that future trials should address. This case study demonstrates the value of systematic reproducibility checks for the transparency of clinical research and the challenges in data sharing for reproducibility.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.