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Background <p>Randomised clinical trial data monitoring is essential for ensuring quality and adherence to protocols, often achieved through on-site methods. However, challenges like the COVID-19 pandemic have highlighted limitations. Central data monitoring offers an alternative, leveraging digital capabilities and diverse expertise.</p> Methods <p>The SafeBoosC-III trial, randomising 1,601 preterm infants across 70 sites, was a pragmatic trial offering detailed education of the investigators before launch. During the trial, we conducted pre-protocolised central data monitoring encompassing monthly data checks, data completeness assessments, and statistical analyses. Discrepancies were primarily identified through visual interpretation of data.</p> Results <p>Throughout the trial, meticulous central monitoring helped keeping missing data at only 1.4%. Of 43,965 data points reviewed, 338 were flagged, with 81 (0.2% of all data and 24.0% of flagged data) corrected after investigation.</p> Conclusions <p>The methodology of combining manual investigation with statistical tools effectively minimised data errors, enhancing validity. This approach in the SafeBoosC-III trial emphasises the importance of ongoing central data monitoring in clinical trials. Standardised protocols for such monitoring are crucial for ensuring data quality and accelerating the validation process in future trials.</p>

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Optimising data completeness and quality in multicentre randomised clinical trials using central data monitoring: a pragmatic approach

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年6月24日

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  • Markus Harboe Olsen,
  • Mathias Lühr Hansen,
  • Janus Engstrøm,
  • Sanam Safi-Rasmussen,
  • Gorm Greisen,
  • Christian Gluud,
  • Adelina Pellicer,
  • Agata Bargiel,
  • Andrew Hopper,
  • Anita C. Truttmann,
  • Anja Klamer,
  • Anne Marie Heuchan,
  • Asli Memisoglu,
  • Barbara Krolak-Olejnik,
  • Beata Rzepecka,
  • Bergona Loureiro,
  • Chantal Lecart,
  • Cornelia Hagmann,
  • Ebru Ergenekon,
  • Eleftheria Hatzidaki,
  • Emmanuele Mastretta,
  • Eugene Dempsey,
  • Evangelina Papathoma,
  • Fang Luo,
  • Gabriel Dimitriou,
  • Gerhard Pichler,
  • Giovanni Vento,
  • Gitte Holst Hahn,
  • Gunnar Naulaers,
  • Guoqiang Cheng,
  • Hans Fuchs,
  • Hilal Ozkan,
  • Isabel De Las Cuevas,
  • Iwona Sadowska-Krawczenko,
  • Jakub Tkaczyk,
  • Jan Sirc,
  • Jinhua Zhang,
  • Jonathan Mintzer,
  • Julie De Buyst,
  • Karen McCall,
  • Klaudiusz Bober,
  • Kosmas Sarafidis,
  • Lars Bender,
  • Laura Serrano Lopez,
  • Lina Chalak,
  • Ling Yang,
  • Luc Cornette,
  • Luis Arruza,
  • Mariana Baserga,
  • Martin Stocker,
  • Massimo Agosti,
  • Merih Cetinkaya,
  • Miguel Alsina,
  • Monica Fumagalli,
  • Olalla Lóepez Suarez,
  • Olalla Otero,
  • Olivier Baud,
  • Pamela Zafra,
  • Peter Agergaard,
  • Pierre Maton,
  • Renaud Viellevoye,
  • Ruth del Rio Florentino,
  • Ryszard Lauterbach,
  • Salvador Piris-Borregas,
  • Saudamini Nesargi,
  • Segundo Rite,
  • Shashidhar Rao,
  • Shujuan Zeng,
  • Silvia Pisoni,
  • Simon Hyttel-Sørensen,
  • Siv Fredly,
  • Suna Oguz,
  • Tanja Karen,
  • Tomasz Szczapa,
  • Xiaoyan Gao,
  • Xin Xu,
  • Zhaoqing Yin

摘要

Background

Randomised clinical trial data monitoring is essential for ensuring quality and adherence to protocols, often achieved through on-site methods. However, challenges like the COVID-19 pandemic have highlighted limitations. Central data monitoring offers an alternative, leveraging digital capabilities and diverse expertise.

Methods

The SafeBoosC-III trial, randomising 1,601 preterm infants across 70 sites, was a pragmatic trial offering detailed education of the investigators before launch. During the trial, we conducted pre-protocolised central data monitoring encompassing monthly data checks, data completeness assessments, and statistical analyses. Discrepancies were primarily identified through visual interpretation of data.

Results

Throughout the trial, meticulous central monitoring helped keeping missing data at only 1.4%. Of 43,965 data points reviewed, 338 were flagged, with 81 (0.2% of all data and 24.0% of flagged data) corrected after investigation.

Conclusions

The methodology of combining manual investigation with statistical tools effectively minimised data errors, enhancing validity. This approach in the SafeBoosC-III trial emphasises the importance of ongoing central data monitoring in clinical trials. Standardised protocols for such monitoring are crucial for ensuring data quality and accelerating the validation process in future trials.