A promising treatment for Covid-19 comes from a most unusual source - an anti-depressant treatment. Is the evidence compelling? What should we believe?
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?
Fast to the wrong answer is not a good business or scientific strategy. Slow, but rigorous, analysis does not meet business or scientific needs either. It has to be "and."
There is a lot of confusion over what data science is and how it is the same or different from statistics or other data analytic fields such as epidemiology or econometrics. This is my attempt to describe the "big tent" of Analytics.
Published research from respectable journals and reported by renowned press outlets can be very misleading and of questionable importance. But it helps keep funding for the researchers and readership for the news media.
Alzheimer's Disease has had many failures, and various companies have had mixed results. Bayesian approaches can bring clarity to the inference and primary question: "Does this treatment work?"
Estimands is the simplest concept with the most difficult implementation. With the release of the final ICH Guideline in Estimands today, this first of several blogs to follow delves into the topic.
Implicit models in the back of our minds can creep into explicit models creating biased predictions that have societal implications.
If we fail to acknowledge that we have biases and assumptions that influence our assessment of 'objective facts,' then we delude ourselves. Our perception of reality and how we judge evidence is colored by our beliefs which arise from our specific experiences.
The probability that the null hypothesis is true is 0.50. How should we interpret that and then write it down mathematically?