The Cytel blog keeps you up to speed with the latest developments in biostatistics and clinical biometrics.
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.
As a recognized expert in adaptive trials, Cytel has extensive experience designing and managing trials with interim analyses. To ensure success in what are often complex studies, data management as well as statistical expertise is required. Cytel data managers are well versed in the various nuances and demands of managing the successful delivery of an interim analysis from a data collection point of view.
Success from the data management standpoint depends on three core elements- effective timeline management, thoughtful database design, and a proactive approach to data cleaning. In this blog, Patti Arsenault, our Global Head of Data Management shares her thoughts.
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.
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.
The Lung-MAP trial is an innovative biomarker driven 'precision medicine' study which evaluates five novel agents for the treatment of patients with advanced squamous cell carcinoma of the lung. As well as exploring therapeutic options for this indication, it also aims to improve the drug development process.
At a Cytel seminar earlier in the year, Antje Hoering of CRAB presented to delegates on some of the practical challenges of the Lung-MAP study.
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.
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.