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It is difficult to define the intention-to-treat (ITT) effect, and thereby the ITT estimand, since the use of the ITT approach can involve treating patients with many different treatments for many different time periods.

Even with the publication of ICVH E9(R1) and so may efforts globally for explaining and implementing the estimand framework, too often statisticians, and clinical researchers in general, focus their efforts on how to handle incomplete data, which is my te…

The word estimand has exploded into the vernacular of statistical thinking for clinical trials in the last decade or so.

Randomization and complete data on the randomized experimental units are needed for cause-and-effect inference.

No. 23: How Bayes Bets on Football The following blog was initiated by Professor Frank Harrell (Vanderbilt University) for which I ultimately gave input and did some writing.

Background Once again, I am writing about Statistics and Data Science as a follow-up to the annual Joint Statistical Meeting held in the beginning of August in Toronto.

Preamble I have written on this topic before (see Blogs 2, 3, 4, 17, 18 and 21), and this is a continuation and clarification of my thinking.

Well, here I go again.

Fluvoxamine for Covid-19 Revisited Background I am writing this blog as a follow-up to a previous blog on fluvoxamine as a treatment for Covid-19 (see Blog 19: We Won’t Get Fooled Again, Again).

I am writing this Blog because I just got done watching a segment on 60 Minutes about the use of fluvoxamine as a treatment for mild COVID-19 patients.