Central data monitoring in the multicentre randomised SafeBoosC-III trial – a pragmatic approach
Markus Harboe Olsen, Mathias Lühr Hansen, Sanam Safi, Janus Christian Jakobsen, Gorm Greisen, Christian Gluud, The SafeBoosC-III Trial Group, Adelina Pellicer, Agata Bargiel, Andrew Hopper, Anita C. Truttmann, Anja Klamer, Anne Marie Heuchan, Aslı Memişoğlu, Barbara Królak‐Olejnik, Beata Rzepecka, Bergona Loureiro, Chantal Lecart, Cornelia Hagmann, Ebru Ergenekon, Eleftheria Hatzidaki, Emmanuele Mastretta, Eugene Dempsey, Evangelina Papathoma, Fang Lou, Gabriel Dimitriou, Gerhard Pichler, Giovanni Vento, Gitte Holst Hahn, Gunnar Naulaers, Guoqiang Cheng, Hans F. Fuchs, Hilal Özkan, Isabel De Las Cuevas, Iwona Sadowska-Krawczenko, Jakub Tkaczyk, Jan Širc, Jinhua Zhang, Jonathan Mintzer, Julie De Buyst, Karen McCall, Klaudiusz Bober, Kosmas Sarafidis, Lars Bender, Laura Serrano Lopez, Lina F. Chalak, Ling Yang, Luc Cornette, Luis Arruza, Mariana Baserga, Martin Stocker, Massimo Agosti, Merih Çetınkaya, Miguel Alsina Casanova, 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 Oğuz, Tanja Karen, Tomasz Szczapa, Xiaoyan Gao, Xin Xu, Zhaoqing Yin
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.