Hi Tingjie, It does not: Sigma squared is the sum of all error variances, and assay error in most cases is only a small contribution to this sum. There are exceptions, but when applying a previous model to new data it is rarely the first modification that comes to my mind.
Given your objectives with the model, maybe the best evaluation would be to obtain individual parameters based on subject’s first visit (IGNORE later time points in $DATA), and then see how well these etas predict the DV at subsequent visits? To split by visit is only an example, obviously, if your project is in anesthesia it may be too late for dose adjustment after the visit is over, and in other cases observations over several days or weeks may be more relevant, for prediction of even later points in time. Best regards Jakob > On 6 Apr 2018, at 21:40, Tingjie Guo <i...@tingjie.name> wrote: > > Small correction in question 2: SIGMA (instead of OMEGA) value influences > individual ETAs... > > > @Leonid, @Jakob, Thank you both for your input. > > @Jakob, You are right, I'm interested in individual ETAs. The idea is to > evaluate the predictive ability of the model in particular subjects (external > data) in order to guide clinical care for these subjects. Does this purpose > alter your opinion on SIGMA choice? > > > Yours sincerely, > Tingjie Guo > > > On Fri, Apr 6, 2018 at 7:51 PM, Leonid Gibiansky <lgibian...@quantpharm.com > <mailto:lgibian...@quantpharm.com>> wrote: > It would be better to use > > $EST METHOD=1 INTERACTION MAXEVAL=0 > > (at least if the original model was fit with INTERACTION option and residual > error model is not additive). > > One option is to use Para = THETA * EXP(ETA) > You would be changing the model, but the model is not too good any way if you > need to restrict Para > 0 artificially. > > SIGMA should be taken from the model. > > Leonid > > > > On 4/6/2018 12:32 PM, Tingjie Guo wrote: > Dear NMusers, > > I have two questions regarding the statistical model when performing external > validation. I have a dataset and would like to validate a published model > with POSTHOC method i.e. $EST METHOD=0 POSTHOC MAXEVAL=0. > > 1. The model added etas in proportional way, i.e. Para = THETA * (1+ETA) and > this made the posthoc estimation fail due to the negative individual > parameter estimate in some subjects. I constrained it to be positive by > adding ABS function i.e. Para = THETA * ABS(1+ETA), and the estimation can be > successfully running. I was wondering if there is better workaround? > > 2. OMEGA value influences individual ETAs in POSTHOC estimation. Should we > assign $SIGMA with model value or lab (where external data was determined) > assay error value? If we use model value, it's understandable that $SIGMA > contains unexplained variability and thus it is a part of the model. However, > I may also understand it as that model value contains the unexplained > variability for original data (in which the model was created) but not for > external data. I'm a little confused about it. Can someone help me out? > > I would appreciate any response! Many thanks in advance! > > Your sincerely, > > Tingjie Guo > >
