@stat12000 wrote: Exclusion of such parameters is not acceptable by the FDA. For such models we are essentially trying to demonstrate that probabilities are high when expected and low also when expected. I clearly understand that the true probability is zero, but a predicted probability cannot be zero; likewise for the predicted probability asymptote for all 1's. I equate this with computation of the statistical power of an effect size. Such a probability has range space 0 < power < 1, exclusive of 0 and 1.   While I have no experience with the FDA, let me say that modeling does not always lead to truth. The truth is, if your data is all zeros, then the probability of zero is 1, regardless of the fact that your modeling method doesn't get that number. In essense, when your data is all zeros, you have the wrong modeling method.   I clearly understand that the true probability is zero, but a predicted probability cannot be zero; likewise for the predicted probability asymptote for all 1's. I equate this with computation of the statistical power of an effect size. Such a probability has range space 0 < power < 1, exclusive of 0 and 1.   Your modeling method fails when there are all zeros in the Y variable. So don't use it.  ... View more