The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
The Society for Clinical Data Management (SCDM) conference brought clinical data managers from around the world to Seattle-Bellevue, WA on September 23-26. The conference offered an unmatched opportunity to discover innovative solutions in the clinical data management industry. In this blog, we will share our data management colleagues' experiences, observed trends and contributions to the program.
In this blog, we share a new infographic based on this popular blog post illustrating some of the critical interactions that need to take place between data management and statistics groups to help ensure efficiency and data quality.
Data management is an essential building block for successful Immuno-Oncology (I-O) trials. At the Immuno-Oncology Clinical Trials operations meeting in New York in earlier this year, Patti Arsenault, VP Quality Assurance at Cytel discussed with Christopher Lamplugh, AVP, Clinical Data Management, Global Data Operations at Merck, the key challenges for data management in the space, and what’s needed to overcome them.
A precise and thorough approach to planning is key for success in data management.
The Data Management Plan (DMP) is a critical document in any data management project. It outlines all of the data management work to be done, the timelines and milestones to be achieved, as well as the outputs to be produced. The DMP lets all of the stakeholders know what to expect, how to expect it and when to expect it.
The Society for Clinical Data Management (SCDM)‘s publication, Good Clinical Data Management Practices (GCDMP) (1), provides a complete chapter on Data Management Plans. (The GCDMP is available, even to non-members of the society, at their webpage). It is important to note that while DMPs are not regulated documents, they are in fact so commonly used across the industry they have become auditable, and therefore scrupulously close attention needs to paid to getting them right.
We outline 4 key points to bear in mind when creating or reviewing a Data Management plan.
Use a Standard Template for Consistency
To a great extent, the DMP can, and should be standardized across projects for a consistent approach. When using a centralized biometrics model, where data services( data management, statistics, statistical programming) are conducted by a single provider, the development of such standard documents can represent an efficiency in the study set up, and also reduce the oversight burden for the sponsor. Indeed, for any trial project, a robust Data Management Plan template provides a solid starting point. One of the important challenges facing industry professionals today is the increasing complexity of clinical trials, and as such, great care needs to be taken to ensure the DMP accurately documents what actions will be taken with the trial data. Having a highly experienced data management team working on your project, with a track record of implementing innovative and complex trial designs, therefore, becomes increasingly important in this environment.
Outsourcing solutions should never be a one size fits all process, and smaller and emerging biopharma companies may have different priorities and processes when working with external vendors to larger pharmaceutical organizations.
December 18th 2016 was a significant date for the pharmaceutical industry and regulatory submissions. For trials which commence after this date, the FDA will no longer accept non-CDISC data submissions for new drug applications ( NDAs) , certain investigational new drug applications, abbreviated new drug applications (ANDAs) and certain biologics license applications (BLAs).
The FDA guidance Providing Regulatory Submissions In Electronic Format — Standardized Study Data (1) also notes that the requirement will include ‘all subsequent submissions, including amendments, supplements, and reports’ to the submission types.
With regard to other regulatory agencies position on CDISC, the Japanese Pharmaceuticals and Medical Devices agency ( PMDA) will now request CDISC compliant submissions after October 2016 with a certain transitional period. This will be fully mandatory by 2018. While the European Medicines Agency (EMA) is adopting a top-down approach and therefore more focused on topics such as data transparency, issues of data integration and interoperability will also form part of the EMA’s future plans.
With this in mind, any sponsor planning an NDA, BLA or other regulatory submission needs to make sure they are observing best practice with regards to CDISC. In this blog, we outline some of the key issues to bear in mind as you prepare for your data submission.
Data Standards play a crucial role in structuring and promoting long term value of clinical data.
Clinical Data Acquisitions Standards Harmonization or CDASH was developed with participation from all three ICH regions (US, Europe and Japan) with recommended data collection fields for 16 domains-> DEMOG, AE etc. It also includes implementation guidelines, best practice recommendations, and regulatory references. There is sometimes a misconception that CDASH defines the layout of the CRF and eCRF. This is not the case. The function of CDASH is to define the naming conventions for the clinical database, and outline how variables are mapped to SDTM. It defines how questions should be formulated for data collection within the CRF and eCRF making use of standard CDISC controlled terminology. In this blog, we will provide an example of CDASH implementation.
The management of quality clinical data collection is built on a number of core essentials- including project management, timeline management, understanding of the deliverables, alignment with statistics and selection of the right technologies. However, clinical development is a complex business and clinical data management approaches must be tailored to meet the specific needs of the trial. In this blog, we take a look at some of the key considerations to be addressed by data management across the different clinical development phases.