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
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
Keywords: Central monitoring, Data quality, Data deviations, Missing data, Clinical trials, Mahalanobis distance