Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Hume, Sam; Aerts, Jozef; Sarnikar, Surendra; Huser, Vojtech

doi:10.1016/j.jbi.2016.02.016

Cited by 41 publications

(32 citation statements)

References 44 publications

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“…The introduction of standardized disease severity scores will facilitate headto-head comparisons and the 'meta-analyzability' of clinical trials and observational studies. Full compliance of the ViVI Disease Severity Score mobile application with Clinical Data Interchange Standards Consortium (CDISC) standards further expands data interoperability and compliance with reporting formats to regulatory agencies [145][146][147][148][149][150].…”

Section: Discussionmentioning

confidence: 99%

Influenza and other respiratory viruses: standardizing disease severity in surveillance and clinical trials

Rath

Conrad

Myles

et al. 2017

Expert Review of Anti-infective Therapy

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Influenza-Like Illness is a leading cause of hospitalization in children. Disease burden due to influenza and other respiratory viral infections is reported on a population level, but clinical scores measuring individual changes in disease severity are urgently needed. Areas covered: We present a composite clinical score allowing individual patient data analyses of disease severity based on systematic literature review and WHO-criteria for uncomplicated and complicated disease. The 22-item ViVI Disease Severity Score showed a normal distribution in a pediatric cohort of 6073 children aged 0-18 years (mean age 3.13; S.D. 3.89; range: 0 to 18.79). Expert commentary: The ViVI Score was correlated with risk of antibiotic use as well as need for hospitalization and intensive care. The ViVI Score was used to track children with influenza, respiratory syncytial virus, human metapneumovirus, human rhinovirus, and adenovirus infections and is fully compliant with regulatory data standards. The ViVI Disease Severity Score mobile application allows physicians to measure disease severity at the point-of care thereby taking clinical trials to the next level.

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Section: Discussionmentioning

confidence: 99%

Influenza and other respiratory viruses: standardizing disease severity in surveillance and clinical trials

Rath

Conrad

Myles

et al. 2017

Expert Review of Anti-infective Therapy

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“…The system includes forms from clinical and research systems and supports multi-lingual data files. A later examination of the ODM model has found that it is being used as a metadata standard for many use cases including Electronic Data Capture and Electronic Medical Records, data collection, data tabulation and analysis, and data archival [9]. Systems needing more features than the ODM model can be developed keeping many of the fundamental metadata design principles of multi-lingual and flexible communication in mind.…”

Section: Discussionmentioning

confidence: 99%

Towards Multi-Lingual Pneumonia Research Data Collection Using the Community-Acquired Pneumonia International Cohort Study Database

Mattingly¹,

Buckner²,

Pena³

2019

JRI

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Sharing the results of clinical research continues to play a large role in scientific discovery [1], and more and more funders and publishers are encouraging investigators to adopt sustainable means to share the data used along with their results. For ad-hoc studies the structure of data is not crucial beyond the needs of accurate analysis. However, when data sharing and reproducibility of research are important, time and effort must be invested to structure a dataset to be efficiently combined with other datasets. Efficient means of integrating data from different sources are needed to leverage the full benefits of data sharing because new research questions can be examined using integrated, multi-source data. Studies that are international in scope can provide large, diverse samples of patient populations, but require investment in translation, and the appropriate ontological structuring of data. In this paper we describe the process of extending the Community Acquired Pneumonia Organization (CAPO) clinical research database to support data collection for multiple languages. After English, Spanish is the most common spoken language of members of the Community Acquired Pneumonia Organization with almost 40% of member sites being in Spanish speaking countries. Starting with Spanish we establish a general multi-language workflow for data entry into the CAPO database, with the eventual goal of supporting all CAPO member languages. Methods The study database for CAPO currently resides in a web-hosted instance of the REDCap electronic data capture software. REDCap is a secure web application for building and managing online surveys and databases. Members of CAPO access the REDCap instance remotely from its web URL https:// id.research.louisville.edu/capo using a web browser and their assigned user credentials. Demographic and clinical history information can then be entered for new cases in the database.

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“…20,21 ODM's hierarchical metadata structure has been used to generate data collection forms, decision support systems, and other applications. 12 ODM provides an extension mechanism to augment the model as needed to support specific interchange scenarios and alternative use cases. Define-XML, one such ODM extension, provides the metadata to describe tabular datasets and plays a key role in establishing traceability in regulatory submission datasets.…”

Section: Trace-xml Development and Evaluationmentioning

confidence: 99%

“…Define-XML, one such ODM extension, provides the metadata to describe tabular datasets and plays a key role in establishing traceability in regulatory submission datasets. 12,22 The FDA's March 2017 Study Data Technical Conformance Guide states that the Define-XML file "describes the metadata of the submitted electronic datasets and is considered arguably the most important part of the electronic dataset submission for regulatory review." 23…”

Section: Trace-xml Development and Evaluationmentioning

confidence: 99%

“…11 Today no tools exist in the clinical research domain capable of tracing a data element from analysis results back to the original, raw data for studies that conform to the current regulatory submission data standards. 12 This paper presents a novel, three-layered traceability framework called Trace-XML that breaks down the existing silos and provides metadata traceability across the clinical research data lifecycle.…”

Section: Introductionmentioning

confidence: 99%

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Enhancing Traceability in Clinical Research Data through a Metadata Framework

Hume¹,

Sarnikar

Noteboom

2020

Methods Inf Med

Self Cite

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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.

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Current applications and future directions for the CDISC Operational Data Model standard: A methodological review

Cited by 41 publications

References 44 publications

Influenza and other respiratory viruses: standardizing disease severity in surveillance and clinical trials

Influenza and other respiratory viruses: standardizing disease severity in surveillance and clinical trials

Towards Multi-Lingual Pneumonia Research Data Collection Using the Community-Acquired Pneumonia International Cohort Study Database

Enhancing Traceability in Clinical Research Data through a Metadata Framework

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