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Statistical Challenges with Site Enrollment in Clinical Trials 

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. 

patricia stephenson
Blog Post

Overcoming statistical challenges in rare disease drug development

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.

Blog Post

Where did the odds ratios go?

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?

Blog Post

Study-Size Adjusted Percentages in Integrated Adverse Event Displays

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.

Blog Post

Submitting SAS Programs to FDA

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.

Blog Post

How to estimate the sample size for your next study from a publication with nothing but p-values and Ns

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.

patricia stephenson
Blog Post

Innovative Designs in Early-Stage Studies

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.