Diversity Action Plans will soon be required for phase 3 clinical trials and other pivotal studies of drugs and biological products conducted in the US. Are you ready for this requirement?
Did you know that insufficient enrollment is the leading cause for clinical trials being halted? Study sponsors rightly embrace those sites which are high performing as they give a study the best opportunity to meet its enrollment targets. However, is it possible for there to be overreliance on these high enrolling sites? Unfortunately, the answer is yes.
Responding to information requests (IRs) from FDA during review of a marketing application can be stressful. How do you survive the rollercoaster of requests? We have outlined useful tips and best practices for managing responses to IRs during FDA review of your marketing application.
Regulatory agencies like the FDA require substantial evidence of the drug’s effectiveness for its intended use and sufficient information to conclude that the drug is safe. However, flexibility is given in how the standard can be met given the challenges associated with the limited number of subjects available in rare disease.
Reviewing recent FDA approvals, you may be struck by the total absence of odds ratios. Browsing the labels from the 2023 novel approvals, you can find proportions, differences in proportions, Chi-Squared analyses, CMH and variants, but logistic regression and odds ratios have practically disappeared from labeling. What gives?
FDA’s Benefit-Risk Framework is the presentation for communicating the assessment of the benefits and risks based on the therapeutic context, available evidence and uncertainties for the product in development.
To those of us who regularly create or review adverse event (AE) incidence tables for randomized controlled trials, it may come as a surprise that your typical AE incidence table can be misleading if data was combined from more than one trial. This is due to “Simpson’s paradox,” which, simply put, is the phenomenon that the mere grouping of data can introduce confounding or bias otherwise not present.
Biometrics teams can generate hundreds, if not thousands, of SAS programs throughout the drug development process. So how do you know which ones to submit to the FDA? And how do you go about submitting the programs?
As statisticians, we’re frequently asked to do power and sample size calculations from very thin data. Often, we’re given nothing but an ancient publication containing a few p-values, an occasional mean, and maybe a standard deviation (if we’re lucky). Back calculating something meaningful in these situations can seem intimidating, but it turns out that most standard sample size software can do the work for us when used unconventionally. We’ve outlined 2 such unconventional uses.
Innovation in early-stage studies presents the best opportunity to streamline the drug development process. Such designs may not only reduce costs and accelerate timelines but give us better flexibility to address the questions of interest in an increasingly evolving clinical development landscape.
