Use of Clinical Data Interchange Standards Consortium (CDISC) Standards for Real-world Data: Expert Perspectives From a Qualitative Delphi Survey (Preprint)

Abstract

BACKGROUND Real World Data (RWD) and Real World Evidence (RWE) have an increasingly important role in clinical research and health care decision making in many countries. In order to leverage RWD and generate reliable RWE, a framework must be in place to ensure that the data is well-defined and structured in a way that is semantically interoperable and consistent across stakeholders. The adoption of data standards is one of the cornerstones supporting high-quality evidence for clinical medicine and therapeutics development. CDISC data standards are mature, globally recognized and heavily utilized by the pharmaceutical industry for regulatory submission in the US and Japan and are recommended in Europe and China. Against this backdrop, the CDISC RWD Connect Initiative was initiated to better understand the barriers to implementing CDISC standards for RWD and to identify the tools and guidance needed to more easily implement CDISC standards for this purpose. We believe that bridging the gap between RWD and clinical trial generated data will benefit all stakeholders. OBJECTIVE The aim of this project was to understand the barriers to implementing CDISC standards for Real World Data (RWD) and to identify what tools and guidance may be needed to more easily implement CDISC standards for this purpose. METHODS We conducted a qualitative Delphi survey involving an Expert Advisory Board (EAB) with multiple key stakeholders, with three rounds of input and review. RESULTS In total, 66 experts participated in round 1, 56 participated in round 2 and 49 participated in round 3 of the Delphi Survey. Their input was collected and analyzed culminating in group statements. It was widely agreed that the standardization of RWD is highly necessary, and the primary focus should be on its ability to improve data-sharing and the quality of RWE. The priorities for RWD standardization include electronic health records, such as data shared using HL7 FHIR, and data stemming from observational studies. With different standardization efforts already underway in these areas, a gap analysis should be performed to identify areas where synergies and efficiencies are possible and then collaborate with stakeholders to create, or extend existing, mappings between CDISC and other standards, controlled terminologies and models to represent data originating across different sources. CONCLUSIONS There are many ongoing data standardization efforts that span the spectrum of human health data related activities including, but not limited to, those related to healthcare, public health, product or disease registries and clinical research, each with different definitions, levels of granularity and purpose. Amongst these standardization efforts, CDISC has been successful in standardizing clinical trial-based data for regulation worldwide. However, the complexity of the CDISC standards, and the fact that they were developed for different purposes, combined with the lack of awareness and incentives to using a new standard, insufficient training and implementation support are significant barriers for setting up the use of CDISC standards for RWD. The collection and dissemination of use cases showing in detail how to effectively implement CDISC standards for RWD, developing tools and support systems specifically for the RWD community, and collaboration with other standards development organizations and initiatives are potential steps towards connecting RWD to research. The integrity of RWE is dependent on the quality of the RWD and the data standards utilized in its collection, integration, processing, exchange and reporting. Using CDISC as part of the database schema will help to link clinical trial data and RWD and promote innovation in health data science. The authors believe that CDISC standards, if adapted carefully and presented appropriately to the RWD community, can provide “FAIR” structure and semantics for common clinical concepts and domains and help to bridge the gap between RWD and clinical trial generated data. CLINICALTRIAL Not Applicable

NIDM-Terms: community-based terminology management for improved neuroimaging dataset descriptions and query

“…Queder et al 10.3389/fninf.2023.1174156 To annotate datasets for future discovery or integration, researchers need to be able to rely on a set of common properties for precisely defining study variables, beyond what is already offered by BIDS for imaging data. In other domains beyond neuroimaging, tools have been developed to aid in dataset annotation such as the open source ISA framework (Sansone et al, 2012) for life sciences research, the Clinical Data Interchange Standards Consortium (CDISC) RDF framework (Facile et al, 2022) focused on the medical and healthcare domains, and Frictionless Data 11 developed to support climate scientists, to humanities researchers, to government data centers, and others. In this manuscript, we focus on the research neuroimaging community yet many of the methods presented are general and could be applied to other domains in synergy with related efforts.…”