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The CDISC Controlled Terminology (CT) defines the terms that may be used to represent clinical trial data in the CDISC standards. Despite its unique importance, there has been limited systematic examination of the coverage of this terminology. In this work, we performed an assessment of the completeness of CDISC CT's coverage by comparing clinical outcomes for multiple sclerosis (MS) available in CDISC CT with two independent high-fidelity benchmarks: (1) 71 expert-selected outcomes catalogued by the National Institute of Neurological Disorders and Stroke (NINDS), and, (2) 66 common outcomes used in MS trials registered on ClinicalTrials.gov (CTG). We employed a semi-automated search and term-mapping process to identify possible CDISC equivalents to the benchmarks' measures. We found that 55% of the NINDS outcomes and 52% of the CTG outcomes are absent from the CDISC Terminology, indicating a need for expanding the terminology to take into account other established standards and real-world practice. [ABSTRACT FROM AUTHOR]

Common outcome sets are vital for ensuring usability of clinical trial results and enabling inter-study comparisons. The task of identifying clinical outcomes for a particular field is cumbersome and time-consuming. The aim of this work was to develop an automated pipeline for identifying common outcomes by analyzing outcomes from relevant trials reported at ClinicalTrials.gov and to assess the pipeline accuracy. We validated the output of our pipeline by comparing the outcomes it identified for acute coronary syndromes and coronary artery disease with the set of outcomes recommended for these conditions by a panel of experts in a widely cited report. We found that our pipeline identified the same or similar outcomes for 100% of the outcomes recommended in the experts' report. The coverage of the pipeline's results dropped only slightly (to 21 out of 23 outcome domains, 91%) when we restricted the pipeline to trials posted before the publication of the report, indicating a great potential for this pipeline to be used in aiding and informing the future development of core outcome measures in clinical trials. [ABSTRACT FROM AUTHOR]

Common Data Elements (CDEs) are necessary for ensuring data sharing across studies, providing comparability, and enabling aggregation and metaanalyses. The process of developing a set of CDEs for a given clinical research area has typically been arduous and time-consuming. In this work we introduce an automated pipeline that can greatly aid the process by identifying, aggregating, and ranking relevant CDEs from the outcomes of studies registered on clinicaltrials.gov (CTG). The pipeline uses the Medical Subject Headings (MeSH) ontology to group and rank candidate CDEs by specific diseases. The initial CDE pipeline has been tested using an emerging research domain. The resulting CDEs output was aligned with the current recommendations in the corresponding subject area. Further development of automated means for CDE generation based on structured information from CTG and MeSH is warranted. [ABSTRACT FROM AUTHOR]