Welcome to the Analytix Thinking blog! The blog that is intended to help people think rightly about data and deciding what is true. First, the intended audience is those who are analytically/quantitatively minded, but the exposition is also meant to be consumable by those who are curious about such matters, but without formal mathematical, statistical … Continue reading No. 1: Introduction – Welcome to Analytix Thinking
Blog 27 – Estimands Part 3B – What Is the ITT Estimand Anyway?
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. Because of this mixture of treatment effects, the ITT approach is more of a HOW for analyzing data, than a WHAT for defining the treatment effect of interest.
Blog 26 – Estimands Part 3A – What Is the Question?
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 term for the missing or confounded data created by so-called intercurrent events. Let’s get the focus on the clinical question of interest.
No. 25 – Estimands Part 2 – What Exactly Is an Estimand?
The word estimand has exploded into the vernacular of statistical thinking for clinical trials in the last decade or so. Where did it come from and why is it so important now?
No. 24: Estimands Part 1 – Just Do ITT?
Randomization and complete data on the randomized experimental units are needed for cause-and-effect inference. What happens when that logic is broken?
No. 23: How Bayes Bets on Football
Bayesian and frequentist approaches to inference or prediction are very different. How different? This simple example highlights the difference and the argument in favor of using Bayesian posterior probabilities.
No. 22B: Whose Boat Is It Anyway?
Statistical Science and Data Science: Don't compete; CREATE!
No. 22A: Statistics and Data Science – The Two Cultures
Building a bridge between the Statistical and Data Science professions, or perhaps recognizing that the two cultures are manifestations of the same essence.
No. 21: Good News, Bad News, Worse News
Everyone likes to have their data analysis work result in some notable findings. Beware! "Torture the data long enough and they will confess to anything."
No. 20: I Am (Probably) Wrong, Maybe
A promising treatment for Covid-19 comes from a most unusual source - an anti-depressant treatment. Is the evidence compelling? What should we believe?
No. 19: We Won’t Get Fooled Again, Again
Many clinician researchers are attempting to "repurpose" old treatments for COVID-19. How shold we evaluate purported positive findings in a small, but rigorous, clinical trial?
analytixthinking.blog 