Prelude to a crossover, part deux

Prelude to a crossover, part deux

The anatomy of a washout, for better or worse.


In blue represents the baseline data.  On the left are the subjects and their body weight prior to randomization.  At baseline in phase I, we can see that the randomization wasn’t perfect, but that doesn’t really matter so much because this is a CROSSOVER study.  Note the group who is assigned to receive active drug first weighs slightly less than those assigned to placebo (98 vs. 102 kg).

The drug causes a 10 kg weight loss and there is no relevant placebo effect.

After a treatment-appropriate washout period, we are back again at baseline but this time for phase II.  Note the body weight of subjects 1-3 at the end of phase I (89, 88, and 87 kg) has returned to normal.  Now subjects 4-6 get the active treatment and experience a similar outcome.  The final summary appears in the column on the right: even though randomization at baseline was imperfect, the differences were crushed by the superiority of the crossover design, and we see the true drug effect regardless of whether we are comparing drug to baseline OR drug to placebo.  Voila, Mucho gusto, and Kudos


Take II.

Everything from baseline until the end of phase I is identical to the above example.  BUT the washout period is inadequate and the group who received active drug during phase I (subjects 1-3) has not returned to baseline and thus exhibits treatment-specific spillover effects.  Subjects 1-3 are at an artificially lower body weight for the baseline measurements of phase II, so the total baseline data are reduced (97.5 kg vs. 100 kg).  Now we get a different answer if we compare drug to baseline or drug to placebo.  This example illustrates one small error, but it is grievous.  Larger errors are made, and they are worse.  at one end of the spectrum, livelihoods and intellectual progress depend on the accuracy of these data.  be prescribed a sub-optimal medication, prescribe a wrong medication, waste time, etc., etc.  failing to account for a particular confounding variable and carelessly (or otherwise) using an improper statistical technique are two very different errs.  (end soapbox diatribe).



calories proper


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