arch2
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I was first exposed to this in the context of baseball batting averages. I'll relate that example in case it helps someone.
{For those unfamiliar with baseball, a player's "batting average" is the number of hits the player has made divided by the number of hit attemps, also known as "at-bats." Ruth and Gehrig were stars in the early 20th century. Ruth did enjoy a drink, and Gehrig never missed a game. The rest is made up.)
In 1927, Gehrig (injured but playing every game) and Ruth (on a months-long drinking tear, therefore sitting out lots of games while staggering through the rest) both performed terribly at the... (read more)
I was first exposed to this in the context of baseball batting averages. I'll relate that example in case it helps someone.
{For those unfamiliar with baseball, a player's "batting average" is the number of hits the player has made divided by the number of hit attemps, also known as "at-bats." Ruth and Gehrig were stars in the early 20th century. Ruth did enjoy a drink, and Gehrig never missed a game. The rest is made up.)
In 1927, Gehrig (injured but playing every game) and Ruth (on a months-long drinking tear, therefore sitting out lots of games while staggering through the rest) both performed terribly at the... (read more)
1) The total number of Gehrig's at-bats for the season in my previous comment should have been 400, not 300 (and, sorry for the duplication)
2) I haven't seen many attempts to actually answer the question in the posting. I'll stick out my neck, after making a couple of simplifying assumptions: ASSUMING that the mortaility frequencies are reflective of the true underlying probabilities, and that the assignment of treatments to patients was otherwise random, I'd use treatment A on men regardless whether they have a history of heart disease, and treatment B on women. (In the real world, where these assumptions don't necessarily hold, I'd have to think a lot harder about the unreliability of the smaller sample sizes, and of course I'd try to find out all I could about further confounding factors, the rules that were used for treatment selection, potential mechanisms underlying the gender and history effects, etc.) Critical comments invited.