Uncertainty in CV's

From: Mats Karlsson <mats.karlsson@biof.uu.se> Subject: Uncertainty in CV's Date: 26 Sep 1997 19:16:25 -0400 As part of his comment to Nick, Stuart said that if omega is large it is likely to be associated with a large statistical uncertainty. This is probably true in general. I would like to add that the confidence intervals around the estimate of omega is likely to be asymmetric. If one estimates the confidence intervals by using the Likelihood profile method, usually one finds that compared to the SE's provided by NONMEM, that both the lower and higher CI's are higher. Thus, if one really cares about the uncertainty in omega estimates the Likelihood profile method may be required. Also, Stuart said that there is no normality assumption made with respect to the distribution of etas (neither for epsilons, I believe). However, many of the suggested validation procedures use the normality assumption (prediction intervals for example). Unless the normality assumption is crucial for the clinical application of the model, such validation procedures seem inappropriate to me. A perfectly valid model may fail a validation test because of an additional assumption never made in the modelling. Another situation where one needs to be concerned with the normality assumption is in clinical trial simulation. Oftentimes it is assumed that the final population model, when turned into simulation mode, can produce real-life like predictions. This may not at all be true if eta's and/or epsilons are non-normally distributed. Mats Karlsson