RE: Describing variability

From: Leonid Gibiansky Subject:RE: [NMusers] Describing variability Date:Wed, 02 Apr 2003 10:45:31 -0500 It looks like we agreed that 1. It is not good to use the model that did not converged. 2. It is not good to use the model that converged but does not provide $COV step. 3. Even if $COV step converged, this is not a guarantee that the model is correct, since it may be ill-conditioned any way. Saying that, I would propose to use common sense: If 1. CL, V, KA etc. estimates are reasonable, 2. PRED vs. DV plot looks good. 3. Variability estimates are within 30-40%. 4. Simulations show good agreements with observed data (i.e., the central line follows population prediction, 90% CI encompass most of the data) 5. Distributions of random effects are in agreement with our assumptions (no bias). 6. No visible tends in eta vs covariates plots (for covariate models). 7. No visible trends in eta vs dose group (if any) plots. 8. All the reasonable measures had been taken to force convergence then accept the model. Otherwise try to reduce/correct it. This diagnostic is more or less independent of the final model properties, although I would 1. Try to get $EST to converged if possible. 2. Try to get $COV step converged if possible 3. Do not accept the model if the relative standard error of estimate or variability is say, more that 100%. After all, this is not a mathematical theorem or a rigid proof. If the model is good for the purposes that are formulated at the start of the analysis, then we may be less strict on the math side. As was said, "All the models are wrong but some of them are useful" Leonid