By Lin Taft, statistics leader, GSK
As the life sciences industry modernizes across all aspects of the field — from electronic health record data to consumer wearables — it is crucial that statistical analytics of clinical trial data for delivering drugs efficiently and safely to patients keeps pace and steps into the modern era as well. This is especially important now for the pharma industry to expand on the options of analytical tools. In this highly regulated field, it is critical to ensure all these tools being used to analyze data as part of clinical trial evidence-gathering efforts can be relied upon to produce accurate and consistent results capable of supporting regulatory submissions.
A Strong Need To Implement More Modernized Capabilities
Last year, TransCelerate developed and launched the Modernization of Statistical Analysis (MSA) Framework to propose a model methodology for demonstrating to health authorities that regulatory submissions generated with any analytical software, including nontraditional analytical tools, provide the reliability necessary to support regulatory decision-making. Prior to the development of this framework, the industry struggled to keep pace with the modernization that was taking place in other aspects of the field. This is because the industry was lacking any widely accepted methodology for demonstrating what can be considered a robust and reliable statistical analytical tool, limiting the options available to industry stakeholders. Now, with newly emerging electronic health record data and other varied data sources, along with the vast quantity of data being collected increasing exponentially, it is of utmost importance that pharma industry participants have access to the most modernized analytics to ensure the capability for automation, advanced statistical methods, machine learning, and more.
As more clinical trial sponsors utilize, adopt, and use the MSA Framework to persuade health authorities, the model’s methodology for validating a modernized analytical environment and the principles underlying the methodology will become more ubiquitous. Feedback from these companies and organizations will be used both to improve on the framework’s aim to support regulatory submissions and to tailor the framework to meet other industry needs and objectives, such as making important clinical development decisions, etc. In the long term, a potential benefit of implementing the framework will be seen at the patient level. Pharma companies may be able to identify molecules faster, review clinical trial data more efficiently, and bring therapies and medications to market more quickly, delivering them to patients who need them in a shorter time span while simultaneously supporting efficacy and safety.
Although many case studies have been developed to highlight the various uses of the MSA Framework and present a case for adoption, I would like to walk through a specific scenario to help illustrate the MSA Framework’s potential benefits when deciding to use a nontraditional tool because of its flexibility or efficiencies. For example, if a pharmaceutical company has decided to use historical trial data in their pivotal study in an effort to make the clinical trial development more efficient and reduce patient burden, and is considering using a nontraditional software, the concern might be in showing that the results are reliable. The framework can be applied here to provide guidance on proving the software’s accuracy and more. By applying the three MSA principles – accuracy, reproducibility, and traceability – to the specific scenario, a company might be able to show health authorities that the results from using the nontraditional tool are reliable and the company can have confidence in their analysis results to support their submission.
Hurdles In Implementing And Validating Novel Statistical Capabilities
Initially, some companies may be concerned about the reliability of any novel nontraditional software and whether health authorities will accept the results generated by them. Generally, global health authorities have not outlined which software packages can and cannot be used in a submission, only that they need to be reliable and that documentation of appropriate software testing procedures should be available. The MSA Framework was focused on meeting the health authorities’ expectations, while giving sponsors the ability to use the most effective analytical tool in each situation by providing a foundation for showing that companies are building a controlled environment where health authorities can have confidence in the statistical outputs produced.
The testing and validation required for a package to be included in this environment, which will be used to support regulatory submission, presents the biggest hurdle for implementing novel statistical capabilities. The amount of resources required is one of the top challenges since it is not possible to build a perfect environment immediately – the environment will be developed continuously. New elements will be tested and added into the environment as needed. Although this is a resource-intensive effort, the potential benefits and efficiency gained afterward are numerous. This, in turn, will motivate the company to eventually use more advanced statistical methods to be more efficient and, at the same time, have the same confidence in the results.
The Importance Of Collaboration And Communication
One important lesson learned from applying the MSA Framework to the analysis and regulatory delivery of clinical trial insights is the importance of collaboration and communication among multiple stakeholders in pharma companies, especially between clinical research and development (R&D) and IT. Typically, these business functions work independently, so they may not understand what each function is doing. However, modernizing the environment requires multiple partners to join forces and work closely together. Communication is key, since not all the stakeholders are familiar with different aspects of the environment. For instance, IT may know how to build the infrastructure, but the R&D business knows what needs to be built. Early engagement from the different stakeholders to create alignment from the beginning and having a dedicated team that understands both sides will be key to success.
When looking to the future, there are opportunities for implementing the MSA Framework to build a modernized analytical environment to apply novel statistical analytics with confidence. As technology continues to advance, the MSA Framework’s principles can be applied to enable the use of the most appropriate analytical tools and to develop safe and efficacious medicines more efficiently for patients.
About The Author:
Lin Taft is a statistics leader at GSK. She is responsible for providing statistical expertise to the design, analysis, reporting, and interpretation of clinical studies and influencing clinical development plans and regulatory and commercial strategies.