Lisa Kammerman, Ph.D.

I spent 24 years at FDA reviewing the statistics in NDA and BLA submissions. Now I write The Significant Difference: what FDA's reviewers see, and what sponsors need to know. Short, practical, from the reviewer's side of the table. www.thesignificantdifference.com

At FDA, I recalculated sponsors' sample sizes from their SAPs. Most matched. Not all. Issue 5: what a reviewer checks in your SAP, and the gaps that become review questions. www.thesignificantdifference.com/issue5

"It depends." That's the answer sponsors get when an FDA meeting question is too broad. Issue 4: how to ask questions FDA can answer, what a meeting package needs, and why the minutes matter. www.thesignificantdifference.com/issue4

A power calculation is only as good as its weakest assumption. As an FDA reviewer, when I saw a treatment difference or a completion rate with no source, I looked harder at everything else. Issue 3: where sample size assumptions go wrong. www.thesignificantdifference.com/issue3

When a submission didn't match its own analysis plan, I always asked: When was this decision made, and what did the data look like at that point? That question ended more conversations than any other. Issue 2: www.thesignificantdifference.com/p/your-sap-i...

The fastest reviews I did had one thing in common, and it wasn't the p-value. It was whether the protocol, the SAP and the reported results told one story. More in Issue 1 of The Significant Difference ↓