Bayesian Method
The Bayesian statistical approach integrates data from previous studies into data collected in a current study through the use of an algorithm called Bayesian Theorem. The integrated data may provide sufficient justification for smaller or shorter clinical studies. Commonly used frequentist statistical methods allow data from previous studies to be factored into new trials at the design stage, but not the analysis stage. When good prior information is available, the Bayesian approach may offer great advantages over the conventional frequentist approach.
The US Food and Drug Administration (FDA) has recently issued guidance on the use of Bayesian methods in medical device clinical trials. "This final guidance on the use of Bayesian statistics is consistent with the FDA's commitment to streamline clinical trials, when possible, in order to get safe and effective products to market faster," said FDA Commissioner Margaret A. Hamburg, M.D. "This is a terrific example of regulatory science in practice at FDA." According to the agency, Bayesian analysis is particularly suited to medical devices because, unlike pharmaceuticals, their effects are usually physical and therefore limited to specific areas rather than systemic. The FDA has approved many medical device PMAs based on clinical studies designed using the Bayesian method.
The RCRI Biostatistics group helps clients evaluate good prior information and justify the use of Bayesian methods in clinical trials, which may save substantial cost and bring the medical device to market faster.
Automatic Reporting
Once the trial ends and the results are known, the submission date is often delayed while the clinical report is compiled. The basic problem with most statistical software is that results cannot simply be pasted into the clinical report template in a validated fashion. RCRI can save you considerable expense and reduce project timelines with our automatic reporting system that transfers statistical output directly into the report.