Implementing an automated monitoring process in a digital, longitudinal observational cohort study

  • Background: Clinical data collection requires correct and complete data sets in order to perform correct statistical analysis and draw valid conclusions. While in randomized clinical trials much effort concentrates on data monitoring, this is rarely the case in observational studies- due to high numbers of cases and often-restricted resources. We have developed a valid and cost-effective monitoring tool, which can substantially contribute to an increased data quality in observational research. Methods: An automated digital monitoring system for cohort studies developed by the German Rheumatism Research Centre (DRFZ) was tested within the disease register RABBIT-SpA, a longitudinal observational study including patients with axial spondyloarthritis and psoriatic arthritis. Physicians and patients complete electronic case report forms (eCRF) twice a year for up to 10 years. Automatic plausibility checks were implemented to verify all data after entry into the eCRF. To identify conflicts that cannot be found by this approach, all possible conflicts were compiled into a catalog. This “conflict catalog” was used to create queries, which are displayed as part of the eCRF. The proportion of queried eCRFs and responses were analyzed by descriptive methods. For the analysis of responses, the type of conflict was assigned to either a single conflict only (affecting individual items) or a conflict that required the entire eCRF to be queried. Results: Data from 1883 patients was analyzed. A total of n = 3145 eCRFs submitted between baseline (T0) and T3 (12 months) had conflicts (40–64%). Fifty-six to 100% of the queries regarding eCRFs that were completely missing were answered. A mean of 1.4 to 2.4 single conflicts occurred per eCRF, of which 59–69% were answered. The most common missing values were CRP, ESR, Schober’s test, data on systemic glucocorticoid therapy, and presence of enthesitis. Conclusion: Providing high data quality in large observational cohort studies is a major challenge, which requires careful monitoring. An automated monitoring process was successfully implemented and well accepted by the study centers. Two thirds of the queries were answered with new data. While conventional manual monitoring is resource-intensive and may itself create new sources of errors, automated processes are a convenient way to augment data quality.

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Author:Lisa LindnerORCiDGND, Anja WeißORCiDGND, Andreas ReichORCiD, Siegfried Kindler, Frank BehrensORCiDGND, Jürgen BraunGND, Joachim Listing, Georg SchettORCiDGND, Joachim SieperGND, Anja Maria StrangfeldORCiDGND, Anne C. RegiererORCiDGND
Parent Title (English):Arthritis Research & Therapy
Publisher:BioMed Central
Place of publication:London
Document Type:Article
Date of Publication (online):2021/07/07
Date of first Publication:2021/07/07
Publishing Institution:Universitätsbibliothek Johann Christian Senckenberg
Release Date:2022/12/20
Tag:Data monitoring; Data validation; Observational study; Spondyloarthritis
Issue:art. 181
Article Number:181
Page Number:7
First Page:1
Last Page:7
The data that support the findings of this study are available from German Rheumatism Research Centre but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of German Rheumatism Research Centre.
RABBIT-SpA is jointly funded by Abbvie, Amgen, Biogen, Hexal, Janssen-Cilag, Lilly, MSD, Novartis, Pfizer, UCB, and Viatris. The study management at DRFZ is independent in the conduct of the study, the analyses, and the publication of the results. Open Access funding enabled and organized by Projekt DEAL.
Institutes:Medizin / Medizin
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften / 61 Medizin und Gesundheit / 610 Medizin und Gesundheit
Licence (German):License LogoCreative Commons - Namensnennung 4.0