Optimizing the Use of Data Standards
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Best Practices for Metadata Documentation (Define-XML vs reviewer's guide). This project is aimed at looking at the use of the reviewer's guide in conjunction with the define.xml. The objective is to define best practices for documenting and describing dataset structures and contents within standard submission deliverables such as define.xml and the data reviewers guide, addressing common challenges, differences between reviewing agencies and divisions, and recommending quality assurance activities. The team plans on delivering at least one white paper on this subject.
Data Reviewers Guide in XML: This project will develop the Data Reviewer's Guide (i.e., SDRG and ADRG) in an XML format for regulatory submissions. Furthermore, this project will identify and develop style sheets, elements and semantics capability. This project will also assess a more cohesive cross-documentation data exchange between the define.xml and data reviewer's guides in an XML format. The project intends to use the existing Analysis Data Reviewer's Guide (ADRG) and Study Data Reviewers Guide (SDRG) as it's basis.
Define-XML v2.0 Completion Guidelines & Style Sheet Recommendations: This project addresses documenting the best practices in the implementation of define 2.0 and the existing style sheet. Two CDISC Define-XML liaisons are involved in this project.
Two Projects have been identified:
1) Define-XML v2.0 Completion Guidelines - The deliverable is a completion document focusing on best practices for content and granularity
2) Define-XML v2.0 Stylesheet Recommendations - there are 2 phases to this project:
a) Development of a Define-XML v2 stylesheet for regulatory submissions, by reviewing and updating the existing CDISC stylesheet
b) Demonstrate other uses of a stylesheet, and utilising libraries and frameworks to make the display more interactive
Clinical Legacy Data Conversion Plan Report: This team will work on a standardised approach to the development of a single traceability document to address conversions from non-standardised data (i.e. legacy) to standardised data (SEND, SDTM, ADaM). Although the Technical Conformance Guide indicates this should be added to the SDRG, FDA has agreed that a separate document is acceptable since the conversion could be related to SDTM and ADaM. There are also other traceability situations that occur within the life cycle of a compound that could be relevant and should be shared with FDA. The goals of the team are to provide a template, completion guidelines, and some examples that may be utilised by sponsors to develop the LDCP. It is expected that LDCP will be created when non-standardised data is converted to standardised data (e.g. CDISC, SDTM, SEND and ADaM).
SDTM ADaM Implementation FAQ: Standard Implementation nuances exist across the multiple available versions of SDTM and ADaM. The project team will establish a framework for the collection, compilation, assessment, responding to, and publishing common SDTM and ADaM implementation challenges/nuances. This is important for Sponsors that are still transitioning to newer versions of standards (e.g. SDTM), as well as the vast majority who have to govern multiple versions, even for a single product or application. This is a joint project between PhUSE and CDISC. 5 primary categorical areas will be used for the FAQ's:
1) Data Submissions (SDTM and ADaM)
2) Validation / Conformance Rules
3) SDTM / ADaM IG Nuances
4) Legacy SDTM Mapping
5) Trial Design Domains
Have a query on SDTM / ADaM? Use this form to submit your questions.
(Re-launched) Standardizing Data within the Inspection Site Selection Process: This group is a revival of a previous project team from a few years ago which focused on FDA's still draft guidance document on inspection site selection datasets. The group has completed a CDISC gap analysis document to understand from where elements can be pulled from CDISC datasets in order for organisations to more easily and efficiently create these requested datasets. FDA has read the gap analysis and will work with CDISC and PhUSE moving forward. The Standard Analyses & Code Sharing Working Group has indicated they will consider standard specs and code.
Study Data Standardization Plan (SDSP): Development of a required study data standardization plan early in the development cycle to optimize implementation of CDISC, SEND, SDTM ADaM data standards. The template has been created and FDA has issued an FR Notice for Intent to Review with comments due by 9th January 2017. Deliverables include the following: Template, Sponsor Implementation Plan, example documents and completion guidelines.
Best Practices for Data Collection Instructions: CDISC's CDASH team provides high level completion instructions related to the core data collection modules. The PhUSE Standard Analysis and Code Sharing Working Group has developed recommended displays (i.e., tables and figures) and associated code for use for analysis for data common across therapeutic areas. For some data domains, having more consistent collection instructions at a more detailed level will potentially lead to increased efficiency (site personnel won't have to learn as many collection methods) and fewer queries (less confusion by site personnel). It will enable sponsors to fully leverage the recommendations and code from the PhUSE Standard Analyses and Code Sharing Working Group. This project will create a white paper documenting challenges and gaps with the CDASH CRF completion as well as recommendations for a future version of CDASH. The white paper will be provided to CDISC for their consideration. The domains that will be addressed are adverse events, medical history, concomitant medications, study disposition, pregnancy, reasons for permanent discontinuations from study treatment.
Pooling WHO Drug B3 Format (New Project): In a March 2015 Federal Register Notice, the FDA encouraged sponsors to provide World Health Organisation (WHO) Drug Dictionary codes for concomitant medication data in investigational studies provided in regulatory submissions. The request indicated that codes should include the drug product trade name (where available), the active ingredient(s) and the Anatomical Therapeutic Chemical (ATC) Class. As indicated in the May 2015 Data Standards Catalog, the expectation for it's requirement begins March 2018. Additional information was provided in the October 2015 Study Data Technical Conformance Guide. Other regulatory agencies have communicated similar notices. In order to facilitate the population of active ingredients, the Uppsala Monitoring Centre, who maintains the WHO Drug Dictionary, updated the dictionary (B3 and C3 formats) to improve the structure for multi-ingredient drugs. While this change will greatly improve the efficiency of reviewing, analysing and reporting medication data, it introduces several challenges when creating an integrated database.
GDPR Impact on Data Collection Practices: (New Project): In May 2016, the new EU General Data Protection Regulation (GDPR) was released and will become applicable in all member states two years after publication on May 25th, 2018. The GDPR was designed to harmonise data privacy laws across Europe, to protect and empower all EU citizens' data privacy and to reshape the way organisations across the region approach data privacy. An important requirement from the GDPR is the minimisation of collection of personal data on basis of necessity. There are also examples of countries (e.g. France) that are putting local legislation in place regarding collection of personal information and specifically Date of Birth (e.g. France: day of birth cannot be collect in any trial with an FPI after August 14th 2017 except for children aged 0-2 years). The goal of this project is to share experiences and approaches for collecting date of birth and other personal information in compliance with regulations. For example, the project will explore the impact of collecting minimal expected information on Date of Birth: by only collecting Year of Birth (collected) and Age (collected or derived) for standard clinical trials. The project will create a best practices white paper to summarise experiences and recommendations. As the project progresses, it may also explore other general EU regulations and aspects of the GDPR such as the GDPR implies that subjects should be allowed to have their data deleted after it was collect which seems to conflict with CGPs requirement of the need for an audit trail.
Industry Experiences Submitting Standardised Study Data to Regulatory Authorities: (New Project): FDA and PMDA require standardised study data for certain regulatory submissions. Industry approaches to meeting these requirements vary across companies. This project provides a collaborative, non-competitive forum for industry to share submission planning, interactions with the regulators, test submissions, regulator feedback, etc. Additionally, this project will explore the development of best practices for biometrics departments to engage with regulators. Historically, biometrics departments have not directly interacted with regulators, but relied on internal regulatory affairs departments as an intermediary. The project will examine different communication use cases and make recommendations as to ensure effective exchange of information.