Central data monitoring in the multicentre randomised SafeBoosC-III trial – a pragmatic approach

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

Dataset

Description

Abstract Background Data monitoring of clinical trials is a tool aimed at reducing the risks of random errors (e.g. clerical errors) and systematic errors, which include misinterpretation, misunderstandings, and fabrication. Traditional ‘good clinical practice data monitoring’ with on-site monitors increases trial costs and is time consuming for the local investigators. This paper aims to outline our approach of time-effective central data monitoring for the SafeBoosC-III multicentre randomised clinical trial and present the results from the first three central data monitoring meetings. Methods The present approach to central data monitoring was implemented for the SafeBoosC-III trial, a large, pragmatic, multicentre, randomised clinical trial evaluating the benefits and harms of treatment based on cerebral oxygenation monitoring in preterm infants during the first days of life versus monitoring and treatment as usual. We aimed to optimise completeness and quality and to minimise deviations, thereby limiting random and systematic errors. We designed an automated report which was blinded to group allocation, to ease the work of data monitoring. The central data monitoring group first reviewed the data using summary plots only, and thereafter included the results of the multivariate Mahalanobis distance of each centre from the common mean. The decisions of the group were manually added to the reports for dissemination, information, correcting errors, preventing furture errors and documentation. Results The first three central monitoring meetings identified 156 entries of interest, decided upon contacting the local investigators for 146 of these, which resulted in correction of 53 entries. Multiple systematic errors and protocol violations were identified, one of these included 103/818 randomised participants. Accordingly, the electronic participant record form (ePRF) was improved to reduce ambiguity. Discussion We present a methodology for central data monitoring to optimise quality control and quality development. The initial results included identification of random errors in data entries leading to correction of the ePRF, systematic protocol violations, and potential protocol adherence issues. Central data monitoring may optimise concurrent data completeness and may help timely detection of data deviations due to misunderstandings or fabricated data.
Date made available1 Jan 2021
PublisherFigshare

Cite this