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January 31, 2008

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Adam

I'm not at all sure I believe that. I've just tried simulating some data to see how obvious a group difference would be on unblinded data.

The difference between treatments would have to be pretty big before you could spot it on unblinded data, and probably much bigger than anyone would reasonably expect in a trial. I simulated data for 1000 patients with a between group difference in a continuous outcome variable equal to the standard deviation of the variable. That's a bigger difference than you would normally expect to see in a clinical trial (although I don't know what difference they were looking for here). This histogram of all the combined data was visually indistinguishable from a normal distribution. The data even passed the Shapiro-Wilk test for the normality of the distribution, even though it was technically bimodal.

So I don't believe that you could look at a distribution on blinded data and rule out a significant difference, just because the distribution looks like the topmost of the 3 curves above.

Alan

Adam is correct. You are looking here at the distribution of individual patient results. The treatment-group means are more tightly distributed, by roughly the square root of the number of patients in the trial. (That's about 55x for a 3,000-patient trial) So it is possible to have a very robust treatment effect and still have a unimodal curve.

On the other hand, seeing a bimodal curve in the blinded data would indicate that there's a treatment effect, unless there's a reason for the underlying data to be bimodal. So "blind" isn't really blind, but only if you see two humps.

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