Analysis and study of the data quality loss risk in clinical trials

V. Ye. Dobrova, K. O. Zupanets, K. L. Ratushna

Abstract


The study of risk is essential to the proper planning of clinical trials (CT). At the same time analysis of information concerning possible inconsistencies is done; it allows to develop a corresponding programme of actions, take steps to minimize them and introduce a framework for further implementation of the risk management concept for quality. In order to identify the factors, which may adversely affect the quality of the CT data, 12 basic factors have been identified and analyzed.
They may affect the risk of inconsistencies when registering and analyzing the data in CT, as well as the quality of the study as a whole. The analysis of the composition of the experts group by the basic parameters has been carried out and the assessment of the significance of the influence of 12 basic factors in the whole group and in the sub-groups of experts distributed according to the experience of participation in clinical trials has been conducted. For expert assessment of basic factors impact the questionnaire has been developed; it includes characteristics of qualifications and the experience of experts interviewed and special issues in the risk assessment. For further analysis of these factors and determination of the probability of occurrence of the possible inconsistencies risks when registering and processing the data in CT it is necessary to distribute the experts into the subgroups mentioned above. It allows to achieve a high level of the intra-group consistency of experts and receive a comprehensive assessment of the relationship of 12 influencing factors and possible risks. Taking into account the level of competence and experience of the experts who participated in the clinical trials, the evaluations of this sub-group expert are of the most interest to our study and it should be the subject of further analysis.


Keywords


clinical trials; data management; risk factor; risk management; data quality

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DOI: https://doi.org/10.24959/cphj.14.1295

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ISSN 2518-1572 (Online), ISSN 1562-725X (Print)