The file observation_example.json lists the Observation resource as described in the Demonstrating the clinical study design with FHIR section in json format. (JSON 2 kb)

ACC/AHA =  American College of Cardiology/American Heart Association; ATS =  American Thoracic Society; CDISC =  Clinical Data Interchange Standards Consortium; NCI =  National Cancer Institute; SF-36 =  Short-form 36 questionnaire.

The size of the Medical Device Validation & Verification Market was valued at USD 1.13 billion in 2024 and is projected to reach USD 2.04 billion by 2033, with an expected CAGR of 8.8 % during the forecast period. Recent developments include: In October 2024, SGS Société Générale de Surveillance SA announced the launch of the new CDISC Open Rules consultancy to assist pharmaceutical organizations in navigating compliance with the latest CDISC standards for clinical trial submissions. This service offers comprehensive support from initial consultation to ongoing evaluation, ensuring high-quality data and regulatory adherence. .

Supplementary tables and figures from the research work of harvesting metadata in a clinical enviroment. The tables include a crosswalk between the 4 different data formats FHIR, OMOP, openEHR and CDISC and a prioritization of the metadata items identified. The figures include the visualization of a priority scoring and an example of the prevented data loss by using the proposed convergence format.

The Data Integration & Imaging Informatics (DI-Cubed) project explored the issue of lack of standardized data capture at the point of data creation, as reflected in the non-image data accompanying 4 TCIA breast cancer collections (Multi-center breast DCE-MRI data and segmentations from patients in the I-SPY 1/ACRIN 6657 trials (ISPY1), BREAST-DIAGNOSIS, Single site breast DCE-MRI data and segmentations from patients undergoing neoadjuvant chemotherapy (Breast-MRI-NACT-Pilot), The Cancer Genome Atlas Breast Invasive Carcinoma Collection (TCGA-BRCA)) and the Ivy Glioblastoma Atlas Project (IvyGAP) brain cancer collection. The work addressed the desire for semantic interoperability between various NCI initiatives by aligning on common clinical metadata elements and supporting use cases that connect clinical, imaging, and genomics data. Accordingly, clinical and measurement data imported into I2B2 were cross-mapped to industry standard concepts for names and values including those derived from BRIDG, CDISC SDTM, DICOM Structured Reporting models and using NCI Thesaurus, SNOMED CT and LOINC controlled terminology. A subset of the standardized data was then exported from I2B2 in SDTM compliant SAS transport files. The SDTM data was derived from data taken from both the curated TCIA spreadsheets as well as tumor measurements and dates from the TCIA Restful API. Due to the nature of the available data not all SDTM conformance rules were applicable or adhered to. These Study Data Tabulation Model format (SDTM) datasets were validated using Pinnacle 21 CDISC validation software. The validation software reviews datasets according to their degree of conformance to rules developed for the purposes of FDA submissions of electronic data. Iterative refinements were made to the datasets based upon group discussions and feedback from the validation tool. Export datasets for the following SDTM domains were generated:

• DM (Demographics)
• DS (Disposition)
• MI (Microscopic Findings)
• PR (Procedures)
• SS (Subject Status)
• TU (Tumor/Lesion Identification)
• TR (Tumor/Lesion Results)

The purpose of the NINDS Common Data Elements (CDEs) Project is to standardize the collection of investigational data in order to facilitate comparison of results across studies and more effectively aggregate information into significant metadata results. The goal of the National Institute of Neurological Disorders and Stroke (NINDS) CDE Project specifically is to develop data standards for clinical research within the neurological community. Central to this Project is the creation of common definitions and data sets so that information (data) is consistently captured and recorded across studies. To harmonize data collected from clinical studies, the NINDS Office of Clinical Research is spearheading the effort to develop CDEs in neuroscience. This Web site outlines these data standards and provides accompanying tools to help investigators and research teams collect and record standardized clinical data. The Institute still encourages creativity and uniqueness by allowing investigators to independently identify and add their own critical variables. The CDEs have been identified through review of the documentation of numerous studies funded by NINDS, review of the literature and regulatory requirements, and review of other Institute''s common data efforts. Other data standards such as those of the Clinical Data Interchange Standards Consortium (CDISC), the Clinical Data Acquisition Standards Harmonization (CDASH) Initiative, ClinicalTrials.gov, the NINDS Genetics Repository, and the NIH Roadmap efforts have also been followed to ensure that the NINDS CDEs are comprehensive and as compatible as possible with those standards. CDEs now available: * General (CDEs that cross diseases) Updated Feb. 2011! * Congenital Muscular Dystrophy * Epilepsy (Updated Sept 2011) * Friedreich''s Ataxia * Parkinson''s Disease * Spinal Cord Injury * Stroke * Traumatic Brain Injury CDEs in development: * Amyotrophic Lateral Sclerosis (Public review Sept 15 through Nov 15) * Frontotemporal Dementia * Headache * Huntington''s Disease * Multiple Sclerosis * Neuromuscular Diseases ** Adult and pediatric working groups are being finalized and these groups will focus on: Duchenne Muscular Dystrophy, Facioscapulohumeral Muscular Dystrophy, Myasthenia Gravis, Myotonic Dystrophy, and Spinal Muscular Atrophy The following tools are available through this portal: * CDE Catalog - includes the universe of all CDEs. Users are able to search the full universe to isolate a subset of the CDEs (e.g., all stroke-specific CDEs, all pediatric epilepsy CDEs, etc.) and download details about those CDEs. * CRF Library - (a.k.a., Library of Case Report Form Modules and Guidelines) contains all the CRF Modules that have been created through the NINDS CDE Project as well as various guideline documents. Users are able to search the library to find CRF Modules and Guidelines of interest. * Form Builder - enables users to start the process of assembling a CRF or form by allowing them to choose the CDEs they would like to include on the form. This tool is intended to assist data managers and database developers to create data dictionaries for their study forms.

The zipped file contains datasets from the FAMOVID clinical trial (Thai Clinical Trials Registry ID: TCTR20230111009; published paper: 10.1016/j.ijid.2024.107021) in CDISC SDTM format.
Data de-identification was performed in compliance with the Health Insurance Portability and Accountability Act (HIPAA) Privacy Rule. In particular, participant and site identifiers were recoded as new, randomly-generated unique identifiers, and all dates were redacted.

The Critical Path for Alzheimer’s Disease (CPAD) data base contains patient-level data from 12,811 patients across 36 clinical trials of AD and MCI. All data has been remapped to a common data standard (CDISC SDTM v3.1.2) such that all the data can be analyzed across all studies. It is openly available to CPAD members, as well as to external qualified researchers who submit, and are approved for, a request for access. All data are fully de-identified.

IntroductionA required step for presenting results of clinical studies is the declaration of participants demographic and baseline characteristics as claimed by the FDAAA 801. The common workflow to accomplish this task is to export the clinical data from the used electronic data capture system and import it into statistical software like SAS software or IBM SPSS. This software requires trained users, who have to implement the analysis individually for each item. These expenditures may become an obstacle for small studies. Objective of this work is to design, implement and evaluate an open source application, called ODM Data Analysis, for the semi-automatic analysis of clinical study data.MethodsThe system requires clinical data in the CDISC Operational Data Model format. After uploading the file, its syntax and data type conformity of the collected data is validated. The completeness of the study data is determined and basic statistics, including illustrative charts for each item, are generated. Datasets from four clinical studies have been used to evaluate the application’s performance and functionality.ResultsThe system is implemented as an open source web application (available at https://odmanalysis.uni-muenster.de) and also provided as Docker image which enables an easy distribution and installation on local systems. Study data is only stored in the application as long as the calculations are performed which is compliant with data protection endeavors. Analysis times are below half an hour, even for larger studies with over 6000 subjects.DiscussionMedical experts have ensured the usefulness of this application to grant an overview of their collected study data for monitoring purposes and to generate descriptive statistics without further user interaction. The semi-automatic analysis has its limitations and cannot replace the complex analysis of statisticians, but it can be used as a starting point for their examination and reporting.

Founded in 2022, Statdoc Consulting operates in Health Tech offering a clinical research platform with services in biostatistics, statistical programming, and CDISC consultation. The company specialises in CDISC-compliant solutions, with a team skilled in transforming complex data into actionable insights through statistical analysis, modelling, and data visualisation. Statdoc Consulting focuses on customised solutions to meet specific client requirements, ensuring precise and efficient statistical programming across various data-driven tasks. The firm is committed to maintaining expertise in delivering high-quality services with a deep understanding of CDISC standards.

Morbidity and mortality attributable to COVID-19 is devastating global health systems and economies. Bacillus Calmette Guérin (BCG) vaccination has been in use for many decades to prevent severe forms of tuberculosis in children. Studies have also shown a combination of improved long-term innate or trained immunity (through epigenetic reprogramming of myeloid cells) and adaptive responses after BCG vaccination, which leads to non-specific protective effects in adults. Observational studies have shown that countries with routine BCG vaccination programs have significantly less reported cases and deaths of COVID-19, but such studies are prone to significant bias and need confirmation. To date, in the absence of direct evidence, WHO does not recommend BCG for the prevention of COVID-19. This project aims to investigate in a timely manner whether and why BCG-revaccination can reduce infection rate and/or disease severity in health care workers during the SARS-CoV-2 outbreak in South Africa...., This dataset was collected in a clinical randomised control trial under the TASK008-BCG CORONA protocol. The trial was conducted in South Africa. This trial was registered with ClinicalTrials.gov, NCT04379336., , # Data from: Safety and efficacy of BCG re-vaccination in relation to COVID-19 morbidity in healthcare workers: A double-blind, randomised, controlled, phase 3 trial

The TASK008-BCG CORONA SDTM datasets contains all the study data collected under the TASK008-BCG CORONA protocol. The data is in the raw format of information captured onto the electronic Case Report Forms from the source documentation.

Description of the data and file structure

The TASK008-BCG CORONA SDTM datasets contain the study data in the CDISC SDTM format. The following CDISC SDTM domains were reported in the datasets:

AE - Adverse Events

CM - Concomitant Medication

DM - Demographics

DS - Disposition

EX - Exposure

IE - Inclusion and Exclusion Criteria

LB - Laboratory Findings

MH - Medical History

SV - Subject Visits

VS - Vital Signs

File Formats: The datasets are in both .CSV and .sas7bdat (include 1 SAS formats. catalogue) Below is the structure of each domain

| AE (Adverse Events) Domain ...

Table illustrating the five different categories the application distinguishes and their calculated statistics and charts.