Enhancing Traceability in Clinical Research Data through a Metadata Framework
Background The clinical research data lifecycle, from data collection to analysis results, functions in silos that restrict traceability. Traceability is a requirement for regulated clinical research studies and an important attribute of nonregulated studies. Current clinical research software tools provide limited metadata traceability capabilities and are unable to query variables across all phases of the data lifecycle.
Objectives To develop a metadata traceability framework that can help query and visualize traceability metadata, identify traceability gaps, and validate metadata traceability to improve data lineage and reproducibility within clinical research studies.
Methods This research follows the design science research paradigm where the objective is to create and evaluate an information technology (IT) artifact that explicitly addresses an organizational problem or opportunity. The implementation and evaluation of the IT artifact demonstrate the feasibility of both the design process and the final designed product.
Results We present Trace-XML, a metadata traceability framework that extends standard clinical research metadata models and adapts graph traversal algorithms to provide clinical research study traceability queries, validation, and visualization. Trace-XML was evaluated using analytical and qualitative methods. The analytical methods show that Trace-XML accurately and completely assesses metadata traceability within a clinical research study. A qualitative study used thematic analysis of interview data to show that Trace-XML adds utility to a researcher's ability to evaluate metadata traceability within a study.
Conclusion Trace-XML benefits include features that (1) identify traceability gaps in clinical study metadata, (2) validate metadata traceability within a clinical study, and (3) query and visualize traceability metadata. The key themes that emerged from the qualitative evaluation affirm that Trace-XML adds utility to the task of creating and assessing end-to-end clinical research study traceability.
Keywordsmetadata - data quality - biomedical research - standards - clinical study report - design science research
The [Dakota State University] University Institutional Review Board, chaired by Jack H. Walters, PhD, granted expedited approval (#2016–2016–114) to conduct this research with human subjects.
Eingereicht: 27. März 2020
Angenommen: 09. Juni 2020
07. September 2020 (online)
© 2020. Thieme. All rights reserved.
Georg Thieme Verlag KG
Stuttgart · New York
- 1 Cheah Y-W, Plale B. Provenance quality assessment methodology and framework. J Data Inform Qual 2015; 5 (03) 9
- 2 Combs V. Expert: data integrity should be part of the return-to-work conversation. TechRepublic, May 15, 2020. Available at: https://www.techrepublic.com/article/expert-data-integrity-should-be-part-of-the-return-to-work-conversation/ . Accessed May 18, 2020
- 3 Sultan J. COVID-19 crisis makes complying with data interoperability a priority. HIT consultant, May 7, 2020. Available at: https://hitconsultant.net/2020/05/07/covid-19-crisis-complying-data-interoperability/#.Xr8xZmhKiM8 . Accessed May 18, 2020
- 4 Nahm M. Data quality in clinical research. In: Richesson RL, Andrews J. , eds. Clinical Research Informatics. London: Springer; 2012: 175-201
- 5 Curcin V, Miles S, Danger R, Chen Y, Bache R, Taweel A. Implementing interoperable provenance in biomedical research. Future Gener Comput Syst 2014; 34: 1-16
- 6 Ludäscher B. A brief tour through provenance in scientific workflows and databases. In: Lemieux V. , ed. Building Trust in Information: Perspective on the Frontiers of Provenance. Berlin: Springer; 2016: 103-126
- 7 Curcin V, Fairweather E, Danger R, Corrigan D. Templates as a method for implementing data provenance in decision support systems. J Biomed Inform 2017; 65: 1-21
- 8 Food and Drug Administration. CDER common data standards issues document version 1.1. 2011 . Available at: http://www.fda.gov/downloads/Drugs/DevelopmentApprovalProcess/FormsSubmissionRequirements/ElectronicSubmissions/UCM254113.pdf . Accessed November 22, 2016
- 9 Chhatre D, Malla A. CDER/CBER's Top 7 CDISC Standards Issues. Silver Spring, MD: Food and Drug Administration; 2012: 24-44
- 10 Berkowitz D. The FDA and Slower Cures: the bureaucratic assault on cancer treatments. Wall Street Journal; 2011
- 11 Valkenhoef Gv, Tervonen T, Brock Bd, Hillege H. Deficiencies in the transfer and availability of clinical trials evidence: a review of existing systems and standards. BMC Med Inform Decis Mak 2012; 12 (01) 95
- 12 Hume S, Aerts J, Sarnikar S, Huser V. Current applications and future directions for the CDISC operational data model standard: a methodological review. J Biomed Inform 2016; 60: 352-362
- 13 Simon H. The Sciences of the Artificial. 3rd ed. Massachusetts: MIT Press; 1996
- 14 Hevner A, March S, Park J, Ram S. Design science in information systems research. Manage Inf Syst Q 2004; 28 (01) 75-105
- 15 Sedgewick R, Wayne K. Algorithms. 4th ed. Upper Saddle River, NJ: Addison-Wesley; 2011: 529-537
- 16 Miner Lite QDA. Free qualitative data analysis software. Available at: https://provalisresearch.com/products/qualitative-data-analysis-software/freeware/ . Accessed November 30, 2017
- 17 CDISC Data Exchange Standards. Data exchange. . Available at: https://www.cdisc.org/standards/data-exchange . Accessed September 2, 2019
- 18 CDISC. Operational Data Model v1.3.2 Specification. 2013 . Available at: https://www.cdisc.org/standards/data-exchange/odm . Accessed April 15, 2017.
- 19 CDISC. Define-XML v2.0 Specification. 2013 ; Available at: https://www.cdisc.org/standards/data-exchange/define-xml . Accessed April 17, 2017
- 20 Ngouongo SM, Löbe M, Stausberg J. The ISO/IEC 11179 norm for metadata registries: does it cover healthcare standards in empirical research?. J Biomed Inform 2013; 46 (02) 318-327
- 21 Stausberg J, Löbe M, Verplancke P, Drepper J, Herre H, Löffler M. Foundations of a metadata repository for databases of registers and trials. Stud Health Technol Inform 2009; 150: 409-413
- 22 Food and Drug Administration. Study data technical conformance guide. 2016 . Available at: http://www.fda.gov/downloads/ForIndustry/DataStandards/StudyDataStandards/UCM384744.pdf . Accessed October 21, 2017
- 23 Food and Drug Administration. Study Data Technical Conformance Guide V3.3. 2017 . Available at: http://www.fda.gov/forindustry/datastandards/studydatastandards/default.htm . Accessed October 21, 2017
- 24 Shankaranarayanan G, Ziad M, Wang RY. Managing data quality in dynamic decision environments: an information product approach. J Database Manag 2003; 14 (04) 14-32
- 25 Shankaranarayanan G, Wang RY. IPMAP: Current state and perspectives. Paper presented at: Proceedings of the 12th International Conference on Information Quality; 2007
- 26 Chee C-H, Yeoh W, Gao S. Enhancing business intelligence traceability through an integrated metadata framework. Paper presented at: ACIS Proceedings; 2011
- 27 Shankaranarayanan G, Wang RY, Ziad M. IP-MAP: representing the manufacture of an information product. In: IQ; 2000: 1-16
- 28 Chee C-H, Yeoh W, Gao S, Richards G. Improving business intelligence traceability and accountability: an integrated framework of BI product and metacontent map. J Database Manage 2014; 25 (03) 28-47
- 29 Hume S, Sarnikar S, Becnel L, Bennett D. Visualizing and validating metadata traceability within the CDISC standards. AMIA Jt Summits Transl Sci Proc 2017; 2017: 158-165
- 30 Guest G, MacQueen KM, Namey EE. Applied Thematic Analysis. Thousand Oaks, CA: Sage Publications; 2011
- 31 Curcin V. Data Provenance as a Method of Supporting Reproducibility in The Learning Health System. Hoboken, NJ: Learning Health Systems; 2016
- 32 CDISC 360. Available at: https://www.cdisc.org/cdisc-360 . Accessed October 8, 2019