Evaluating Clinical Trial Data Conventions for the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM)

Lead Investigator: Zhen Lin, Robot Bacon Corporation
Title of Proposal Research: Evaluating Clinical Trial Data Conventions for the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM)
Vivli Data Request: 6869
Funding Source: None
Potential Conflicts of Interest: None

Summary of the Proposed Research:

The Observational Health Data Sciences and Informatics (OHDSI) collaborative is an organization focused on harmonizing observational health datasets. As part of this effort, the group has instituted a data model called the Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) designed to describe a common data format for these datasets.

This project is to evaluate how revisions to OMOP CDM represents clinical trial data from the Vivli database, and identify potential information that is not currently captured in the model revision proposals.

Clinical trials test potential treatments in human volunteers to see whether they should be approved for wider use in the general population. A treatment could be a drug, medical device, or biologic, such as a vaccine, blood product, or gene therapy. Different data submission standards have been adopted by governing agencies in different countries and geographical regions, including the United States of America (USA) Food and Drug Administration (FDA), the European Commission (EC) European Medicines Agency (EMA), Japan Pharmaceuticals and Medical Devices Agency (PMDA), and China National Medical Products Administration (NMPA).  These different data representations create challenges for researchers to systematically analyze clinical trials across these data sources to either re-evaluate the original hypothesis or explore data for new discoveries.

The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a possible solution to enable such systematic examination by transforming/standardizing these heterogeneous data sources into one common representation (terminologies, vocabularies, coding schemes.)  However, information unique and crucial to clinical trials, such as study information and arm design, is not currently modeled in OMOP CDM. Through preliminary study on synthetic clinical trials datasets, we have identified eight areas to be enriched in OMOP CDM to harmonize existing clinical trial submission data standards.  With selected clinical trials from Vivli, we will validate these eight areas to be revised in OMOP CDM to capture clinical trials data thoroughly, evaluate data elements within each area, and identify new areas or data elements based on real world datasets.

Statistical Analysis Plan:

Data analysis will be performed in order to quantify the feasibility of the proposed data transformation methodology.

Data Preparation
Initial preparation of data will require the handling of missing data and yield a count of all valid and/or imputed data elements.

Handling of Missing Data
Will handle on a case by case basis depending on the data
(refer to FDA guidelines)

The following records will be excluded:
Records associated with patients that do not have a year of birth
No person/subject id

The following data will be imputed:
Dates Records missing event dates (will follow the protocol’s SAP guidelines for imputing dates)

Data Analysis
Data analysis involves the calculating of statistical counts of data elements, by type, that were successfully transformed and counts of data elements that were unsuccessfully performed. Percentages of successfully transformed data elements and unsuccessfully transformed elements will be derived from the statistical counts.

Requested Studies:

A Phase II, Randomized, Double-Blind, Placebo-Controlled, Multicenter Study to Evaluate the Safety and Efficacy of MSTT1041A or UTTR1147A in Patients With Severe COVID-19 Pneumonia
Data Contributor: Roche
Study ID: NCT04386616
Sponsor ID: GA42469

An Open-Label Phase 3b Study to Assess Mucosal Healing in Subjects With Moderately to Severely Active Crohn’s Disease Treated With Vedolizumab IV
Data Contributor: Takeda
Study ID: NCT02425111
Sponsor ID: MLN0002-3028

A Randomized, Double-Blind, Chronic Dosing (24 Weeks), Placebo-Controlled, Parallel Group, Multi-Center Study to Assess the Efficacy and Safety of PT003, PT005, and PT001 in Subjects With Moderate to Very Severe COPD, Compared With Placebo
Data Contributor: AstraZeneca
Study ID: NCT02343458
Sponsor ID: PT003014

A Randomized, Double-Blind, Placebo-Controlled Insulin Tolerance Test Study to Assess the Safety, Tolerability, and Pharmacodynamics OF Pitolisant in Patients With Type 1 Diabetes
Data Contributor: Ferox Therapeutics
Study ID: NCT04026750
Sponsor ID: FPITO-T1D-01.01

A Phase 2, Multicenter, Randomized, Double-blind, Placebo-controlled Study to Evaluate a Potassium Normalization Treatment Regimen Including Sodium Zirconium Cyclosilicate (ENERGIZE)
Data Contributor: AstraZeneca
Study ID: NCT03337477
Sponsor ID: D9480C00005

Randomised, Double-Blind (Sponsor Open), Placebo-Controlled, Multicentre, Dose Ranging Study to Evaluate the Efficacy and Safety of Danirixin Tablets Administered Twice Daily Compared With Placebo for 24 Weeks in Adult Participants With Chronic Obstructive Pulmonary Disease (COPD)
Data Contributor: GlaxoSmithKline
Study ID: NCT03034967
Sponsor ID: 205724

A 2-part Randomized, Double-blind (Sponsor-unblinded), Placebo-controlled, Ascending Dose and Parallel Group Study of TLR4 Agonist (GSK1795091) Administered to Healthy Subjects
Data Contributor: GlaxoSmithKline
Study ID: NCT02798978
Sponsor ID: 204685

Efficacy of GSK Biologicals’ Candidate Tuberculosis (TB) Vaccine GSK 692342 Against TB Disease, in Adults Living in a TB Endemic Region
Data Contributor: GlaxoSmithKline
Study ID: NCT01755598
Sponsor ID: 115616

Safety and Immunogenicity Study of GSK Biologicals’ Investigational Recombinant Chimpanzee Adenovirus Type 3-vectored Ebola Zaire Vaccine (GSK3390107A) in Adults in Africa
Data Contributor: GlaxoSmithKline
Study ID: NCT02485301
Sponsor ID: 202091

Summary of Results:

We were unable to bring our analysis to completion due to challenges in utilizing the platform and accessing data to scale up our analysis.

We have no results/findings to share. Ultimately, we were unable to find a way to get our work done given the privacy constraints enforced. We never exported clinical trial results data at most we exported summary meta data about the results (for example, how often a procedure occurred during the study). However, even in these cases, we were unable to export the data we needed to complete our research and ultimately put this effort on hold.