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

(2021) 21:160 Olsen et al. BMC Med Res Methodol https://doi.org/10.1186/s12874-021-01344-4 Open Access RESEARCH Central data monitoring in the multicentre randomised SafeBoosC‑III trial – a pragmatic approach Markus Harboe Olsen1,2*, Mathias Lühr Hansen3, Sanam Safi1, Janus Christian Jakobsen1,4, Gorm Greisen3, Christian Gluud1,4 and The SafeBoosC-III Trial Group 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. Keywords: Central monitoring, Data quality, Data deviations, Missing data, Clinical trials, Mahalanobis distance *Correspondence: 1 Copenhagen Trial Unit, Centre for Clinical Intervention Research, The Capital Region, Copenhagen University Hospital - Rigshospitalet, Copenhagen, Denmark Full list of author information is available at the end of the article Introduction ‘Good clinical practice data monitoring’ of clinical trials is a tool to ensure high quality and accuracy of the data, and adherence to the trial protocol [1, 2]. Quality © The Author(s) 2021. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativeco mmons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data. Olsen et al. BMC Med Res Methodol (2021) 21:160 and accuracy of the data is threatened by random and systematic errors. Random errors include clerical errors and missing data (when missing at random), and primarily reduce statistical power [3, 4]. Systematic errors, however, may create bias and skew the results [3, 4]. The primary causes of systematic errors are misinterpretation, misunderstandings, and fabrication of data. Hence, these should therefore be the primary focus of data monitoring [3, 5]. Data monitoring with on-site monitors increases trial costs and is time consuming for the local investigators [6–8]. Moreover, during the present COVID-19 pandemic, on-site monitoring has been complicated due to health risks and the different lockdown restrictions [9]. On-site monitoring also has the disadvantage of focusing on data by a case-by-case review, and thereby primarily addressing random errors [10, 11]. In most clinical trials, the local investigators are solely responsible for ensuring quality and accuracy of the data and adherence to the protocol throughout the trial – as checked by on-site monitors [12]. The digital revolution has paved the way for the possibility of central data monitoring which can give the coordinating investigator a role in ensuring data quality. Central data monitoring may be conducted in many ways, and should optimally be carried out by a central data monitoring group comprising different competences [2, 11, 13]. This group should not assess safety or interventional effects, as this is a task for the Data Monitoring and Safety Committee (DMSC) [14]. This allows the central data monitoring group to remain blinded to group allocation throughout the lifetime of the trial and focus on identifying missing and ‘odd’ data/data patterns, thereby helping to ensure high quality and accuracy of the data on a running basis to replace the work of ‘data cleaning’ operations at the end of the trial. Hence, the central data monitoring group will identify deviations from the protocol and allow timely corrections and improvements of the electronic participant record form (ePRF). This study aims to outline our approach on the implementation of time-effective central data monitoring for the SafeBoosC-III randomised clinical trial to optimise quality control and quality development [15], and present the initial 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 evaluati (...truncated)