The points aren't the same.
Say you have a group with no duplicates and you calculate the q-value.
If you add a duplicate who survived, you're adding extra Exposed to risk, so the q-value will go down.
If you added a duplicate who dies, you're adding both extra exposed to risk and a death. The addition of a death is much more significant, and the q-value will go up.
Now if you do a proper study you might have millions of lives and for duplicates to make a difference, you'll need loads of duplicates. Arguably, the duplicates should on average cancel each other out. Some of the assumptions aren't as valid anymore though, eg independant lives, so you get more variation. There are other negative effect of duplication. Whether they actually make a practical difference or not depends on the degree of duplication.
If everyone had exactly two policies it wouldn't matter. But if only people of a certain kind had multiple policies, then it may skew the results.
Last edited by a moderator: Mar 21, 2009