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

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@article{a6dec39601fa401fbdc5fe42f71b82af,
title = "Central data monitoring in the multicentre randomised SafeBoosC-III trial - a pragmatic approach",
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 = "Humans, Infant, Newborn, Infant, Premature, Monitoring, Physiologic, Central monitoring, Data deviations, Missing data, Clinical trials, Data quality, Mahalanobis distance",
author = "Olsen, {Markus Harboe} and Hansen, {Mathias L{\"u}hr} and Sanam Safi and Jakobsen, {Janus Christian} and Gorm Greisen and Christian Gluud and {SafeBoosC-III Trial Group}",
note = "{\textcopyright} 2021. The Author(s).",
year = "2021",
month = jul,
day = "31",
doi = "10.1186/s12874-021-01344-4",
language = "English",
volume = "21",
pages = "160",
journal = "BMC Medical Research Methodology",
issn = "1471-2288",
publisher = "BioMed Central Ltd",
number = "1",

}

RIS

TY - JOUR

T1 - Central data monitoring in the multicentre randomised SafeBoosC-III trial - a pragmatic approach

AU - Olsen, Markus Harboe

AU - Hansen, Mathias Lühr

AU - Safi, Sanam

AU - Jakobsen, Janus Christian

AU - Greisen, Gorm

AU - Gluud, Christian

AU - SafeBoosC-III Trial Group

N1 - © 2021. The Author(s).

PY - 2021/7/31

Y1 - 2021/7/31

N2 - 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.

AB - 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.

KW - Humans

KW - Infant, Newborn

KW - Infant, Premature

KW - Monitoring, Physiologic

KW - Central monitoring

KW - Data deviations

KW - Missing data

KW - Clinical trials

KW - Data quality

KW - Mahalanobis distance

UR - http://www.scopus.com/inward/record.url?scp=85112340719&partnerID=8YFLogxK

U2 - 10.1186/s12874-021-01344-4

DO - 10.1186/s12874-021-01344-4

M3 - Journal article

C2 - 34332547

VL - 21

SP - 160

JO - BMC Medical Research Methodology

JF - BMC Medical Research Methodology

SN - 1471-2288

IS - 1

M1 - 160

ER -

ID: 67052087