3.4 years ago by
United Kingdom / London / Francis Crick Institute
I agree with Ryan's support of the 'intersection' approach you suggested, in cases where you wish your effect to be distinguishable from both types of control, but you'll be subject to two rounds of exposure to statistical errors, with the subsequent loss of power. I think it might be worth looking at other approaches also, depending on the definition of 'placebo' here. If the placebo is inducing some partially confounding effect (a scrambled siRNA vector,...) then that is the true control, as your three conditions are then O=baseline, N=baseline+placebo_effect, T=baseline+placebo_effect+biological effect, and N-T gives you the straight biological effect, and you can effectively ignore the untreated group (apart from it's contribution to the estimation of within-group noise). Eliminated things that don't have an O vs T significant effect could in this situation decreases your power and potentially introduces bias against genes where the biological effect is working to counteract the placebo effect (only the experimenter will know if a gene that is upregulated in response to placebo, and reverts to baseline on full treatment is interesting or not).
At the other end of the spectrum, you could actually pool your two types of control
groups <- factor(c(0,0,0,0,0,0,2,2,2)) so that you'd be including any placebo effect as part of the 'replicate' variability, and anything that survives this increase in variability is a sufficiently large biological effect that it dwarfs any placebo-induced variability.
When I get vaguely-specified controls, I tend to apply all three approaches, and draw the expression profiles of genes that are significant in some but not all of the approaches - the experimenter then has a clear visualisation of the different hypotheses being tested.