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


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Olsen M. H. , Hansen M. L. , Safi S., Jakobsen J. C. , Greisen G., Gluud C., ...More

Bmc Medical Research Methodology, no.31, pp.1-10, 2021 (Peer-Reviewed Journal)

  • Publication Type: Article / Article
  • Publication Date: 2021
  • Doi Number: 10.1186/s12874-021-01344-4
  • Journal Name: Bmc Medical Research Methodology
  • Journal Indexes: Science Citation Index Expanded, Scopus, Academic Search Premier, CINAHL, EMBASE, MEDLINE, Directory of Open Access Journals
  • Page Numbers: pp.1-10

Abstract

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