Sponsors often rely on Standardized MedDRA Queries (SMQs) to group adverse events for detection of safety signals across clinical trials. Additionally, the FDA has introduced their own version of adverse event groupings – FDA MedDRA Queries (FMQs). So, what’s the difference?
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.
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?
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.
Ben Vaughn, Rho’s Chief Strategist, Biostatistics & Protocol Design, supports Tonix Pharmaceutical’s analyses of how sleep disturbances impact posttraumatic stress disorder (PTSD). Their research sheds light on a novel treatment, TNX-102 SL, which targets sleep quality and emotional memory processing.
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.
When running a randomized clinical trial, if there are factors which are known during the study design phase to influence study results, it may be advantageous to utilize a stratified randomization which ensures each prognostic factor is balanced between treatment arms.
