By Aubrey Clayton
Dr. Clayton is a mathematical statistics researcher and a parent to three children under 4. He’s the author of “Bernoulli’s Fallacy: Statistical Illogic and the Crisis of Modern Science.”
As a parent of three children under 4, I was hit hard by last month’s announcement that the Food and Drug Administration was delaying its review of Pfizer-BioNTech’s Covid-19 vaccine for children under 5.
Like many caregivers guarding young children against the coronavirus, my winter has been full of rapid tests, mask reorders and outdoor play dates in borderline frostbite conditions. I’m able to manage this because I believe it’s temporary; we just need to hold out a little longer until our children can get vaccinated.
But because I study statistics, I’m also racked with concern that if the data had been assessed in a more nuanced way, we might be putting vaccination appointments on the family calendar right now.
It’s unclear why the F.D.A. paused the review. The most recent data hasn’t been shared, and reporting suggests Pfizer found that the Omicron wave led to many more infections than previously seen in its clinical trial. The decision was made to wait for data on the third dose. Perhaps the two doses were not effective enough for the full group, though earlier data had suggested the vaccines produced a desired immune response for children ages 6 months to 24 months.
The bigger issue, as I see it, is in general statistical methods that are often relied on to evaluate the effectiveness of vaccines and drugs. The standard approach used in almost all clinical trials and endorsed by the F.D.A. requires new drugs to meet an arbitrary statistical threshold, the one people who have taken stats classes may recognize as statistical significance. This is appealing because it serves as a standardized final exam that experimental results all have to pass, unaided by preconceptions on the part of the reviewers or special pleading by the experimenters.
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