Clinical Trial Registries

Background

FAIR implementation is one of the fundamental enablers for secondary reuse of clinical data, thereby realising much greater value from our data assets. FAIR clinical data and metadata will be findable (discoverable) by both machines and humans, any access restrictions will be determined by open protocol standards and supported by highly expressive FAIR vocabulary or ontology standards, including formats. These attributes of Findability, Accessibility and Interoperability together support Reusability which includes the attributes of data usage, provenance and community standards as illustrated in Figure 10.

Open

Figure 10: FAIR as an enabler for secondary use of clinical data. This is supported by  clinical data being sufficiently Findable, Accessible, Interoperable and Reusable for action by machines and humans.

Registries for clinical interventional and observational studies are important resources for supporting the secondary use of clinical data. Examples include Clinical trial registries such as those provided for the FDA by the NIH (https://clinicaltrials.gov ), the EU (https://www.clinicaltrialsregister.eu ) and Cochrane (https://www.cochranelibrary.com/central) which are within the scope of this guide. For each study, they usually include a study description, the study protocol and summary data for the trial results

In order to assess how FAIR these clinical registers are now, and what improvements could be made, breast cancer and Type 2 diabetes have been selected as use cases. These use cases are also representativeattractive because they are often found in the therapeutic portfolios of many pharmaceutical companies. Furthermore, there is much evidence in the literature for comorbidity between the two conditions see for instance Zhao et al. (2016), Yoon SJ et al. (2015) and Samuel SM et al. (2018).

FAIR assessment of clinical trial registries

ClinicalTrials.gov

The NIH funded registry, https://clinicaltrials.gov is the largest open clinical trials database which has been available online since the year 2000. In the web-based user interface, Basic text search (e.g. see query results for “Diabetes Mellitus, Type 2”), is provided along with the advanced search that provides additional search facets, e.g. eligibility criteria, locations or start and end date of a study. 

The registry does offer an API for bulk download of the study methods (metadata) and results summary (data). The available actions and (meta)data elements available from the API corresponds to the advanced search interface and structured data submission. 

For assessment of FAIR maturity we selected two examples, completed clinical trial study with results for each of 1) Diabetes Mellitus Type 2, NCT01020123 (URL or URL for beta) and 2) Female Breast Cancer, NCT00633464 (URL and URL for Bbeta) as shown in Table 4. Note that the content of the beta website is identical to the current, so the assessment of FAIR maturity applies to both.

Table 4: Assessment of ClinicalTrials.gov using the simplified FAIR Maturity Indicators based on two example completed studies with results for the two selected disease conditions.

Findable The study identifiers, NCT01020123 and NCT00633464 are global, unique, persistent and resolvable by machine (GUPRI) which has been incorporated into the Uniform Resource Locator (URL: https://clinicaltrials.gov/ct2/show/study/NCT01020123 ) which is discoverable by Google search. This satisfies the four FAIR maturity indicators for Findability (F1, F2, F3 and F4) at the study level (Table X). The same studies can be retrieved as xml which corresponds to the tabular view in html to expose the structured metadata in a machine-readable format.

Accessible The results summary for this clinical trials registry is at the study rather than patient level so access is unrestricted which does not require authentication or authorisation for viewing (Table 4). The persistence policy for the results summary data and study details metadata is likely to be long term although this is not stated explicitly on the history and policies page.

Interoperable.Even though the metadata for the study description is structured (tabular view or xml), it consists of much free text and almost no identifiers for vocabulary or ontology terms can be found. In this regard, the FAIR interoperability of the clinicaltrial.gov registry could be much improved. Further opportunities to make this registry more FAIR include: 1) JSON Linked Data API instead of plain JSON, 2) Adoption of the openAPI/SmartAPI (SmartAPI ) specifications for FAIR services and 3) Convert XML based data schema to an ontology powered schemaConvert data schema (XSD) to an Ontology., Tconvert term lists could be converted to SKOS-CVs, align/cross-map to dominant FAIRsharing terminologies. Adoption of the CDISC vocabulary is also likely to improve this clinical trials registry (Table 4).

Reusable. The use of data is described at https://clinicaltrials.gov/ct2/about-site/terms-conditions which is protected by international copyright outside the USA or some third parties. Guidance is given to study record managers on submission to ClinicalTrials.gov using the Protocol Registration and Results System (PRS). This provides the only mechanism for expression of provenance and maintaining the expected standards, policies and legal obligations for this community. This mechanism would benefit from use of the provenance ontology, PROV and by adoption of the major clinical standard, CDISC. 

EU Clinical Trials Register

The EU Clinical Trials Register (EU CTR) comprises of method protocols and summary results information for interventional clinical trials similar to Clinical Trials.gov.

Basic text search for “Diabetes Mellitus, Type 2” yields more than 1500 results (see figure YY), a summary of the study level data for every search hit is displayed, including e.g. identifiers for the trial, the sponsor name, the title of the trial, Mmedical condition, Ddisease, Ppopulation age. Additional metadata and summary data for the trial results is organised in the following categories, similar to Clinical Trials.gov:

• Trial Information

• Subject disposition

• Baseline characteristics

• End points

• Adverse events

• More information

The graphical user interface also offers more advanced search besides text search with logical operators, to enable filter of the results by facets such as Country, Age Range, Trial Status, Trial Phase or Gender.

 EU CTR allows the user to download trial results after successful search as a plain text rather structured file which mirrors the free text pdf format for the results, see figure ZZ. However CTR does not offer API access for bulk download.

For assessment of FAIR maturity we compared the same two examples from completed clinical trial studies with results for each of 1) Diabetes Mellitus Type 2, 2009-012612-41 (URL) [=NCT01020123] and 2) Female Breast Cancer, 2007-005209-23 (URL) [=NCT00633464] as shown in Table 5.

Table 5: Assessment of EU CTR using the simplified FAIR Maturity Indicators based on two example completed studies with results for the two selected disease conditions.

Findability: The study identifiers, 2009-012612-41 (=NCT01020123) and 2007-005209-23 (=NCT00633464) are unique and resolvable by machine but the globality and persistence of these identifiers is open to question. These identifiers have been incorporated into the URL (e.g. https://www.clinicaltrialsregister.eu/ctr-search/search?query=2009-012612-41 ) which can be found using Google search. This partially satisfies the four FAIR maturity indicators for Findability (F1, F2, F3 and F4) at the study level (Table 5). 

Accessibility: The results summary for this clinical trials registry is at the study rather than patient level so access is unrestricted which does not require authentication or authorisation for viewing (Table 5). The persistence policy for the results summary data and study details metadata is likely to be long term although this is not stated explicitly. It is notable that CT registry limits user access to a graphical user interface, so there is no programmatic access (i.e. no API) for batch download, unlike http://clinicaltrials.gov .

Interoperability: The study methods and results data and metadata are only available in an unstructured format as plain text or as pdf. The content is identical to that found in ClinicalTrials.gov for the two example trial records. This means there are almost no identifiers for vocabulary or ontology terms. Clearly, the FAIR interoperability of the EU CTR is a massive opportunity for improvement.

Reusable: The use of data is described at https://www.clinicaltrialsregister.eu/about.html which is protected by legal rights described at https://www.clinicaltrialsregister.eu/disclaimer.html . EU CTR would benefit from use of a provenance ontology, such as PROV and by adoption of the major clinical standard, such as CDISC.

Cochrane Library Central for Controlled Trials

The Cochrane Library Central for Controlled Trials (CENTRAL) is a registry of controlled trials, owned by the publisher John Wiley & Sons Ltd. Most CENTRAL records are taken from bibliographic databases (mainly PubMed and Embase.com) and they are also derived from other sources, including CINAHL, ClinicalTrials.gov and the WHO's International Clinical Trials Registry Platform. Therefore, CENTRAL is best seen as a secondary registry for clinical trials (about CENTRAL).

Table 6: Assessment of CENTRAL using the simplified FAIR Maturity Indicators based on two example completed studies with results for the two selected disease conditions.

Findability: The study identifiers, CN-01526935 (=NCT01020123), URL and CN-01517484 (=NCT00633464), URL are local identifiers rather than GUPRI. These identifiers have been incorporated into the URL (e.g. https://www.cochranelibrary.com/central/doi/10.1002/central/CN-01526935/full) but it is not discoverable using Google search. This does not satisfy the FAIR maturity indicators for Findability (F1, F2, F3 and F4) at the study level (Table 6.

Accessibility: The registry record is limited to an abstract, info keywords and related content at the study level, based on the primary source, ClinicalTrials.gov. So access to the content does not require authentication or authorisation for viewing . The persistence policy for the results summary data and study details metadata is not stated explicitly but likely to depend on the primary source. Furthermore, persistence will be subject to the business decisions of a commercial publisher. This only partially satisfies the FAIR maturity indicators for accessibility (Table 6).

Interoperability: The registry content is available as plain text and in numerous structured formats, much like bibliography registers such as PubMed. The content is a condensed derivative from the primary source, ClinicalTrials.gov for the two example trial records. This means there are almost no identifiers for vocabulary or ontology terms which would improve the FAIR interoperability, thereby adding value to CENTRAL.

Reusable:Cochrane has an open access policy for data usage. As a secondary registry, CENTRAL is likely to be dependent on its primary sources for provenance and clinical standards.

Clinical Real World Data

Definitions of clinical Real World Data and Real World Evidence

Real-world data (RWD) is a general term for data that results from the effects of health interventions (such as benefits, risks or resource use) that are not collected in the context of double-blind, randomised, controlled trials (RCTs).

While definitions may vary, RWD tends to be structured, having ‘data models’ with data residing in a fixed field, for example in databases and spreadsheets. RWD can be collected both prospectively and retrospectively from observations of routine clinical practice. Data collected may include, but are not limited to, clinical and economic outcomes, patient-reported outcomes and health-related quality of life.

The analysis of RWD is undertaken to derive Real World Evidence (RWE) which underpins the effectiveness of existing therapies to help plan studies of new medicines. In addition, the RWE of safety outcomes is frequently required by regulatory agencies after marketing authorisation.

GetReal RWE Navigator

The RWE Navigator from the IMI GetReal project includes sources of RWD which lists five types of RWD sources, as summarised in the Ttable below, which also considers the structure expected for the type of RWD source.

Table 7 : types of RWD sources

The registries and databases for RWD are highly structured whereas these are mostly free text for social media and patient-powered research networks. A formidable response to meet this challenge for the rapid proliferation of rare disease registries was the formation of the European Joint Programme on Rare Diseases which includes numerous resources to support FAIRification of RWD (FAIRification ). As a result Kodra et al (2018) published the paper entitled “Recommendations for Improving the Quality of Rare Disease Registries”: Recommendations for Improving the Quality of Rare Disease Registries .

ClinicalTrials.gov

Besides interventional clinical trials, ClinicalTrials.gov also includes over 3,009 available and completed observational studies with results which can be seen as a more organised form of RWD. ClinicalTrials.gov defines oObservational studies as “a type of clinical study in which participants are identified as belonging to study groups and are assessed for biomedical or health outcomes. Participants may receive diagnostic, therapeutic, or other types of interventions, but the investigator does not assign participants to a specific interventions/treatment.” - as defined here.

As of October 2022, ClinicalTrials.gov includes available and completed observational studies with results for the selected use cases, Type 2 diabetes (N = 81) and breast cancer with female filter (N = 65). The assessment of FAIR maturity described previously applies to the observational studies too. ClinicalTrials.gov also has three additional observational studies originating from patient registries for Type 2 diabetes and none for breast cancer. 

Global Alliance for Genomics and Healthcare

Global Alliance for Genomics and Healthcare (GA4GH) is a source of standards and tools for real world genomic and health-related data with over 600 organisations. These are mostly academic institutions, SMEs and a few major Pharmaceutical companies. GA4GH lists a collection of Driver Projects which are real-world genomic data initiatives. 

Below are the current GA6GH driver projects (n=24 on 1st June 2022):

1. All of Us Research Program, 

2. Australian Genomics, 

3. Autism Sharing Initiative, 

4. BRCA Challenge, 

5. Canadian Distributed Infrastructure for Genomics (CanDIG), 

6. Clinical Genome Resource (ClinGen), 

7. ELIXIR Beacon, 

8. ELIXIR Cloud and AAI, 

9. ENA / EVA / EGA, 

10. EpiShare, 

11. EUCANCan, 

12. European Joint Programme on Rare Disease, 

13. GEnome Medical alliance Japan (GEM Japan), 

14. Genomics England, 

15. Human Cell Atlas, 

16. Human Heredity and Health in Africa (H3Africa), 

17. ICGC-ARGO, 

18. Matchmaker Exchange, 

19. Monarch Initiative, 

20. National Cancer Institute Cancer Research Data Commons (NCI GDC), 

21. National Cancer Institute Genomic Data Commons (NCI GDC), 

22. Swiss Personalized Health Network (SPHN), 

23. Trans-Omics for Precision Medicine (TOPMed), 

24. Variant Interpretation for Cancer Consortium (VICC).

The Driver Projects have helped GA4GH to guide their development efforts, pilot tools and advocate, mandate, implement and use relevant frameworks and standards, such as phenopackets (http://phenopackets.org and GA4GH news). The documentation for Phenopackets schema includes a link to the FHIR Implementation Guide, Phenopacket building blocks, a  list of 10 recommended ontologies and three complete examples for Rare Disease, Cancer and a COVID-19 case report.

How FAIR are these sources of real world clinical data at the study level?

The Genomic Data Toolkit collection of GA4GH adopts and shares open standards for genomic data sharing and is made available through Github repositories for code, example files and data. It also provides recommendations for various API's, data file formats, BED Specifications, Data Use Ontologies (DUO) and many more resources.

GA4GH driver projects are discoverable via the general web link: What we do and Google search. The data for each GA4GH driver project are available through controlled data access procedures, such as used by dbGaP, which supports look up,; for example the specific TOPMed Whole Genome Sequencing study. These studies will release data according to patient consent and scope as described in each individual study, in which patients have expressed their wishes for which  projects the data will and will not be allowed to be used.

Data in the GA4GH Driver project studies are FAIRified to varying degrees for each initiative. For example, TOPMed (NHLBI Trans-Omics for Precision Medicine) has implemented FAIR for several of its participating studies. The FAIR maturity assessments have been undertaken using the FAIRshake tool which finds that most of these studies lack a study identifier which is global, unique, persistent and resolvable by machines. This is an obvious opportunity for improvement. A further example, which we have undertaken a simplified FAIR assessment for, is the Australian Genomics data collection, as described next.

Table 8: Assessment of the Australian Genomics Data collection using the simplified FAIR Maturity Indicators based on the data catalogue and web site. For the collection.

Findability: The Australian Genomics data web site is findable with Google search. Data capture and standardisation is addressed on a web page, which makes no mention of the FAIR data maturity. There is a catalogue for all the datasets which has no identifiers and takes the form of a downloadable Excel workbook with a sheet for each dataset. An obvious opportunity for improvement is to assign each dataset, which is an observational study, an identifier, ideally GUPRI.

Accessibility: A typical example of FAIR maturity for accessibility is the Australian Genomics data where access for secondary reuse can be applied for using application forms available for download for each disease area. A catalogue snapshot as an Excel file. No open standard access protocol is evident which is a clear opportunity for improvement.

Interoperability: For the Australian Genomics data, the data catalogue sheets indicate that many of the datasets have data field structure and include or map to ontologies such as HPO and SNOMED. Although this is encouraging, more in depth assessment of interoperability requires application for access to a particular dataset via one of the application forms, which is beyond the scope of this guide.

Reusability: For the Australian Genomics data, the policy for data access and sharing for secondary use, data access and sharing agreement and data breach policy are all available on a web page.

Challenges and a relevant initiatives for RWD and RWE

The example of Australian Genomics data illustrates the many challenges and opportunities to improve the FAIR maturity of these clinical genomic datasets for secondary reuse.  We expect this finding will be all too common for clinical RWD, even at the study rather than patient level. A relevant paper from Cave et al. 2019 is entitled “Real‐World Data for Regulatory Decision Making: Challenges and Possible Solutions for Europe”.

The European Medicines Agency (EMA) and some national medicines agencies have been investing in access to RWD. Building on this experience, in 2021, the European Medicines Regulatory Network (EMRN) initiated plans to create an EU-wide distributed network of RWD named the Data Analytics and Real World Interrogation Network (DARWIN EU). Similarly, the US FDA has their Real Word Evidence Program which includes documentation for the RWE Program RWE Program Framework, published in 2018. Ideally, such multinational RWD initiatives should be guided by FAIR principles, rather than creating clinical data silos which are all too often lost to secondary reuse.

Other registries and efforts

We just want to briefly mention and link out to other relevant data registries and efforts, without going into further details or FAIR assessments:

• The Translational Data Catalog, a joint effort among IMI-FAIRplus, IMI-eTRIKS and ELIXIR-Luxembourg, tries to improve FAIRness for (IMI) datasets and encourage data sharing. It embeds http://schema.org and bioschemas, which is a lightweight but effective approach. The catalog distinguishes “project”, “studies” and “datasets” and plans to make all those searchable in the future (e.g. see search for “Type 2 diabetes” here).

• The International Clinical Trials Registry Platform (ICTRP) is another secondary trial register, created by the WHO, which makes data from multiple sources available. The search portal can be found at https://trialsearch.who.int/ .

The European Medicines Agency published a list of metadata for Real World Data catalogues. This list contains metadata elements for describing real-world data (RWD) sources and observational studies.The chosen metadata will be included in a catalogue of data sources containing information about existing real-world databases (to replace the current ENCePP catalogue) and information about the studies performed on the data sources (to replace and enhance the current EU PAS Register). More information can be found at https://www.ema.europa.eu/en/documents/other/list-metadata-real-world-data-catalogues_en.pdf