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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/21724

Title: Data-driven risk identification in phase III clinical trials using central statistical monitoring
Authors: Timmermans, Catherine
Venet, David
Burzykowski, Tomasz
Issue Date: 2016
Abstract: Our interest lies in quality control for clinical trials, in the context of risk-based monitoring (RBM). We specifically study the use of central statistical monitoring (CSM) to support RBM. Under an RBM paradigm, we claim that CSM has a key role to play in identifying the "risks to the most critical data elements and processes" that will drive targeted oversight. In order to support this claim, we first see how to characterize the risks that may affect clinical trials. We then discuss how CSM can be understood as a tool for providing a set of data-driven key risk indicators (KRIs), which help to organize adaptive targeted monitoring. Several case studies are provided where issues in a clinical trial have been identified thanks to targeted investigation after the identification of a risk using CSM. Using CSM to build data-driven KRIs helps to identify different kinds of issues in clinical trials. This ability is directly linked with the exhaustiveness of the CSM approach and its flexibility in the definition of the risks that are searched for when identifying the KRIs. In practice, a CSM assessment of the clinical database seems essential to ensure data quality. The atypical data patterns found in some centers and variables are seen as KRIs under a RBM approach. Targeted monitoring or data management queries can be used to confirm whether the KRIs point to an actual issue or not.
Notes: [Timmermans, Catherine] Catholic Univ Louvain, Inst Stat Biostat & Sci Actuarielles ISBA, Voie Roman Pays 20, Louvain La Neuve, Belgium. [Venet, David] Univ Libre Bruxelles, Inst Rech Interdisciplinaires & Dev Intelligence, Brussels, Belgium. [Burzykowski, Tomasz] Hasselt Univ, Interuniv Inst Biostat & Stat Bioinformat, Diepenbeek, Belgium. [Burzykowski, Tomasz] Int Inst Drug Dev, Louvain La Neuve, Belgium.
URI: http://hdl.handle.net/1942/21724
DOI: 10.1007/s10147-015-0877-5
ISI #: 000370159000006
ISSN: 1341-9625
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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