CROS NT explains how choosing the right statistical method accompanied by the right technology for your clinical study design can reduce timelines and lower costs while maintaining the quality and validity of a study.
In recent years, the pharmaceutical industry is witnessing a significant increase in costs for the development of new drugs. Companies site excessive study lengths and the high probability of failure as problematic areas for clinical studies.
Choosing the appropriate statistical method is crucial to success. The Bayesian Framework allows statisticians to increase their knowledge about a trial in a dynamic way as soon as new data or evidence is available. Thanks to the Bayesian method, prior information and new experimental data are combined to produce the so-called “posterior probability”.
This approach can reduce timelines and overall costs in a variety of situations. For instance, it can help in the determination of the sample size, and the calculation can be more realistic and accurate using the prior information available at the start of the trial as well as what has emerged during the trial which increases the efficiency and reduces the cost of having an excess number of patients.
Bayesian models can be easily applied to Adaptive Designs, sharing the same idea of translating information into a decision as quick as possible. Therefore, studies doomed for failure can be terminated earlier to reduce the number of patients being treated with a non-effective drug.
Objectives normally requiring two separate studies can be achieved in a single trial by collecting information in the first stage which is immediately used in the second part of the trial. This has an obvious impact on the time to market, ultimately providing a quicker response to relevant medical needs. At each stage the probability of success can be quantified and the study team can be fully informed to evaluate the risks and benefits associated with each decision.
The Bayesian Model is even well suited for medical device trials. The FDA has encouraged the application of Bayesian methods as a way to cut costs in medical device trials emphasizing that “good prior information is often available” and “effects can sometimes be predictable from prior information on the previous generations of a device when modifications to the device are minor”.
Incorporating Technology Solutions with a Bayesian Framework
In order for biostatisticians to make these crucial “go/no-go” decisions, they need access to real time data. With modern technology such as smartphones and tablets, data can be collected from patients at multiple sites and sent directly to a validated EDC system for improved quality data and statisticians can immediately begin making their estimates on adaptive measures like drug dose allocation, sample size calculation and possible termination. Technology solutions have even been developed to take into account adaptive designs and Bayesian models such as IVRS systems of trial supply management systems that can automatically adjust to design changes.
Business Intelligence tools are the best technology option for Data Visualization, especially if implementing a Risk-Based Monitoring approach. They allow for data retrieval, report development from different data sources, report delivery and cloud-based technology. Web-based applications can be accessed from various devices including PCs/laptops, smartphones and tablets.
CROS NT Biostatistics & Technology Solutions
Interested in how a Bayesian Framework can be adapted to your study?
CROS NT has expert biostatisticians that offer consultancy on a case-by-case basis to determine study design and methodology needs. We also offer a portfolio of technology solutions – including an integrated eClinical platform (EDC, ePRO, CTMS, IWRS), various EDC solutions and a clinical data visualization portal – to provide an ideal solution for statisticians needing access to real-time data.
If you’d like to schedule an initial consultation about study design and discuss your upcoming drug or device trial needs, fill out our RFI form.