Bioequivalence and Bioavailability Forum

Dear d_labes,

❝ What about this one: Subtract the 'known (true) means' from the measurement values and analyze them by fitting a model with all the other effects you are supposing to have to account for. The MSE of the fit is then the residual std error I think.

thanks for your input. I will try and see if I can make it work.

❝ But I must confess that I'm totally unsure what you attempt to do in your original question .

❝ You never know the true T/R ratio. Even if you have done your study (first stage or pivotal). The best you have is an estimate.

❝ And assuming a true T/R ratio of say 0.95 within the 2-stage design evaluation is not for fitting a model to the data but only for powering the second stage enough under the assumption that you get an estimate equal or better than this true ratio.

When we calculate type I errors and power in Potvin's scenarios we assume a T/R. This is a 'known', in contrast to the estimates we get from the sampled data. My point is this:
When we calculate the sample size for the second stage we apply a known (=assumed) T/R and a measured variability, but the measured variability reflects the sampled T/R. I am therefore thinking we could empirically try to calculate a maximum likelihood estimate of the variability given the known (=assumed T/R) rather than the observed T/R and given the observations.

So, in essence one suggestion is to somehow fixed the two first parameters of the effects vector and allow the rest to be fit, and see what variability comes out at the other end. If it works the same to subtract known effects from the sampled data then I am happy. Ís the resulting s biased?