Lisa Kammerman, Ph.D.
@kammerman.com
Lisa Kammerman, Ph.D. — FDA statistical consultant and former FDA Master Statistical Reviewer, with 24 years reviewing NDA and BLA submissions.
Newsletter: thesignificantdifference.com
Consulting: kammerman.com
Newsletter: thesignificantdifference.com
Consulting: kammerman.com
Pinned
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
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
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...
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...
What FDA Reviewers Look For in a Statistical Analysis Plan (SAP)
Former FDA Master Statistical Reviewer on what makes a statistical analysis plan credible: estimands, multiplicity, deviations, and early FDA review.
www.thesignificantdifference.com
October 1, 2026 at 6:30 PM
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...
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 ↓
It was whether the protocol, the SAP and the reported results told one story.
More in Issue 1 of The Significant Difference ↓
The first thing an FDA statistician looks for (it's not the p-value)
What FDA's reviewers see — and what sponsors need to know
www.thesignificantdifference.com
September 24, 2026 at 3:30 PM
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 ↓
It was whether the protocol, the SAP and the reported results told one story.
More in Issue 1 of The Significant Difference ↓
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
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
September 23, 2026 at 10:20 PM
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
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