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.
Jul 27, 2018 7:53:00 AM
Apr 25, 2018 10:13:00 AM
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.
Mar 15, 2017 8:51:00 AM
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.
Feb 15, 2017 9:33:05 AM
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.
Dec 23, 2016 8:21:00 AM
As we prepare to say 'so long, farewell' to 2016, we'd like to take the opportunity to thank all our blog readers and subscribers. Read on for a round up of our most read topics from the year....
Dec 21, 2016 9:45:00 AM
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.
Dec 20, 2016 9:21:00 AM
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.
Dec 6, 2016 10:18:00 AM
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.
Oct 7, 2016 8:29:00 AM
Adaptive designs have the potential to accelerate clinical development, and improve the probability of trial success. While the principle is simple- to reduce the uncertainty in clinical development by obtaining additional information from the ongoing trial- the statistical methodologies can be complex, and expert support is often required to conduct the clinical trial design. There's also complexity in the data collection itself, so knowledgable data management support is needed to successfully execute an innovative trial design. In this blog, we take a look at 5 top considerations for successful adaptive trial data management.
Aug 2, 2016 10:30:00 AM
Editor's note( this blog was refreshed in April 2018)
As CDISC compliant submissions become increasingly expected, biopharmaceutical companies are considering how to approach the issue of data standards governance. Standards governance is a lynchpin in the management of CDISC compliance and is important for promoting standards awareness within organizations. It’s also an acknowledged hot topic in the industry.
It has traditionally been common practice for biopharma companies to outsource their CDISC conversion of legacy data for the purpose of publications and submissions to expert CROs. While large biopharma organizations may have dedicated in-house teams deployed to the management of standards governance, the dynamic nature of CDISC requirements means companies can struggle to find the resources to keep up to date and provide the best interpretation of the documentation. Outsourcing can be an option to ensure dedicated staff are available to manage and monitor these aspects and ensure companies remain submission ready.
Jul 28, 2016 10:06:00 AM
To close a clinical database right the first time you have to begin with study start-up. Clearly, you can’t close a database if the data is not cleaned and you can’t have clean data unless you know what is most important for analysis. It’s imperative that data management works closely with the statistics group during CRF/ eCRF design to ensure data is being collected and data checks are being written in a meaningful fashion. But that’s still not enough. The data should be cleaned on a regular basis and forms locked as soon as the data has been SDVd and reviewed. Even then, it will be important to have your statistics team run listings and tables early on to catch anything unexpected. If the data is cleaned and locked by the time the last patient visit comes around then getting Principal Investigator sign-off and ultimately closing the database can run much more smoothly and quickly.
Database lock is a significant milestone in the clinical trial, upon which further data analysis and reporting timelines depend. The Clinical Data Manager is responsible for steering the data management process to ensure that the database is locked on time, and correctly. In this blog we lay out the 6 steps to database lock success.
Jul 12, 2016 9:00:00 AM
How do you go about selecting the best Electronic Data Capture (EDC) system for your study? There is now a vast amount of choice in the market, and many factors to take into account before making your decision. Different stakeholders within the business may also have different perspectives, so any decision making process needs to balance these disparate needs.
Apr 26, 2016 11:30:00 AM
In this blog we’ll highlight some unique challenges that are encountered from a Data Management perspective when working on early phase Oncology trials. We’ll also discuss approaches which can be employed to mitigate these issues.
Apr 20, 2016 8:00:00 AM
During the course of any clinical trial, there are often data which, while collected electronically, are outside of the scope of the eCRF . These data include central lab results like ECGs, PK/PD data and others. In this blog we’ll take a look at some key considerations in handling electronic data transfers and any subsequent integration with the core EDC database.
Apr 15, 2016 9:00:00 AM
It's critical for biostatistics and data management to be closely aligned and working effectively together. The consequences when these biometrics teams aren't integrated can be significant- impacting on both efficiency and data quality. If data is collected and cleaned without the input of statistics, the assumptions which have been made may not be adequate, resulting in additional work and compromised timelines. So, let's take a closer look at 5 important interactions between the two functions during the course of a clinical trial.
Feb 26, 2016 9:00:00 AM
Remember the early days of Electronic Data Capture? Those first systems, which were revolutionary for their time featured basic data entry screens, simple edit checks and a handful of reports.
Technology has come a long way since then, and the EDC landscape has matured dramatically along with it. Current EDC must-haves include easy user and site management, secure automated password retrieval, robust reporting, query and SAE management, as well as Source Data Verification, and Risk based monitoring. Many sites need to be active at the same time; labs need to enter or upload data directly into the system; medical coding should be performed inside of the tool; images need to be uploaded and stored; email and text alerts sent to investigators and sponsors, and patient reported outcomes and diaries need to be available.
Counter intuitively, while EDC system functionality is becoming ever more sophisticated, the interface to build the systems is becoming easier to use and understand.