See this is fascinating to me. You are the statistician I originally replied to, and yet you also won't clearly answer the questions! This is like pulling tee

See this is fascinating to me. You are the statistician I originally replied to, and yet you also won't clearly answer the questions! This is like pulling teeth, but the implication seems to be my take on this statistical test was basically correct. > Difficulty of matching subgroup counts grows rapidly with number of dimensions. ... and the conditions are independent, you'll need to go through 2^12 = 4096 people [for 12 conditions] Except many of these are "primary" diagnoses, which presumably you have one of hence the name, so it's not possible for a person to have both a primary "Pneumonia" diagnosis and a primary "Other" diagnosis. So for each person maybe you're matching 2^3 or 2^4, not 2^12 - in any case, far less than your conclusions are based on. > You'd have to churn through 10s or 100s of thousands of sepsis patients to get your matching subset. So if the matching was 1/1000th as hard as you thought it was, that would be 10s or 100s of patients. A million cases a year, several doctors in major metro areas, there's probably 10s to 100s of patients at any given time in their hospital systems. > Consecutive means you don't skip anyone who has the condition you're trying to treat. Sepsis is very serious and common, so they'd have to treat them all basically simultaneously with the experimental treatment. I'm no expert, but I'd expect they'd want to be able to abort the trial if people started dying from it. It also doesn't even make sense as a study, because you want to test like for like as much as possible; maybe the treatment works fantastically on patients with cirrhosis and nobody else. I think a plausible scenario is each day before normal rounds they did a med search for a patient matching the next one from first group, generally found a good match, worked with their doctor to change the treatment and added them to the study. Maybe two in a day, or skipping a day, and a month later they have really good matching data. Lots of cases to choose from, many exclusive variables. What do the statistics say for a charitable interpretation? Pretty good odds, right? So maybe they didn't report their methods accurately, maybe it was assumed from domain knowledge or was a mistake when they cut and pasted from a template. Could be fraud too, but that seems like a huge leap to be certain of.