Improving the quality of drug research or simply increasing its cost? An evidence‐based study of the cost for data monitoring in clinical trials

Abstract

WHAT IS ALREADY KNOWN ABOUT THIS SUBJECT• Current knowledge is based on practical experience. This paper presents the first novel, quantitative and qualitative view on the efficacy of the sponsor query system widely applied in the pharmaceutical industry, used for quality control purposes by data management for clinical data. WHAT THIS STUDY ADDS• This study presents a structured view on the process of clinical data management and sponsor queries, the efficacy and cost effectiveness of sponsor queries and dual data entry and the conclusion to advocate a more evidence-based approach to clinical data validation. AIMProcedures for verification of data from clinical studies are intended to maintain reliability for clinical trial results. Guidelines or legislations relating to clinical data management are of limited value and no study has yet demonstrated its effectiveness. METHODSponsor queries and dual entry procedures from one CRO on three different phase I trials are analysed on content, impact and cost. RESULTIn this study, sponsor queries and dual entry procedures proved time and cost inefficient in detecting data discrepancies. CONCLUSIONWe advocate a more evidence-based approach for enhancing data integrity throughout the process of clinical data management.

Impact of a targeted monitoring on data‐quality and data‐management workload of randomized controlled trials: A prospective comparative study

“…Another interesting point was the cost analysis, which confirmed that the cost of data quality assurance is linked to both monitoring and DM, and that lowering monitoring requirements increases the DM workload. We showed that, provided our assumption of the cost of a query is reliable, the differential cost per patient could reach more than 100 euros, which is consistent with Pronker's assumptions in an evidence-based study on clinical data-management cost [28]. Beyond the financial analysis, we need to consider the typology of the data corrected with SDV and with DM.…”

Impact of source data verification on data quality in clinical trials: an empirical post hoc analysis of three phase 3 randomized clinical trials

“…A related analysis was performed by Pronker and colleagues focusing on sponsor queries, defined as discrepancies identified in data entered in the eCRF while the trial was on going. The authors concluded that very few queries were sent regarding data points which were critical to the trial end points (0.001% of all data points), leaving a poor cost‐effectiveness ratio and the authors encouraged the development of a risk‐assessment oriented monitoring of clinical trial data .…”

Investigating the Efficacy of Clinical Trial Monitoring Strategies: Design and Implementation of the Cluster Randomized START Monitoring Substudy

“…One recent study examined the usefulness of 100% SDV in a cancer trial dataset from 75 sites in the United Kingdom [9]. The discrepancies found when comparing the data before and after the 100% SDV were mostly random errors which, because evenly distributed across arms, had no significant impact on the primary outcomes.. Another study reviewed sponsor queries from the datasets of three different Phase I studies conducted by one contract research organization in the Netherlands [10]. Only 0.4% (6/1389) of the query-driven data changes could have influenced the primary study results.…”