Priors and covariate model building

Dear NMusers, I am trying to model a sparse dataset by using the benefit of previously published parameter estimates (based on rich data sampling). When applying the $PRIOR subroutine, the THETAs and ETAs estimates of the new dataset are reasonable and the model fit satisfactory. My question now relates to covariate modeling when a prior is applied. No significant covariate relationships are included in my prior model (apart from allometric scaling). The prior was derived based on rich PK sampling but a fairly small sample size. The later sparse sampling study is conducted in a larger group compare to the previous study. This might render us a greater power to detect covariate relationships based on this dataset. Or problem lies in that we do not know how we can correctly conduct a covariate model search with this model? The parameter estimates of the prior are conditioned on the covariate distribution in the dataset on which it was derived and are not necessarily relevant when a covariate relationship is included. Perhaps there is no ideal solution but we would be grateful for any ideas on how to best conduct covariate model building when a prior is used. Best regards, Palang Chotsiri & Martin Bergstrand Mahidol-Oxford Tropical Medicine Research Unit, Bangkok 10400, THAILAND Ps. Ideal is of course to model both datasets together but that might not always be possible for practical reasons.

Dear Palang, If you plan to build a covariate model for a parameter, it must mean that you have reasonable information about this parameter in a rather large sample of patients. It doesn't sound that you like a prior for this parameter - there should be enough info in the data you have available. Best regards, Mats Mats Karlsson, PhD Professor of Pharmacometrics FIRST WORLD CONFERENCE ON PHARMACOMETRICS, 5-7 September 2012, Seoul (www.go-wcop.org) Dept of Pharmaceutical Biosciences Faculty of Pharmacy Uppsala University Box 591 75124 Uppsala Phone: +46 18 4714105 Fax + 46 18 4714003

Dear Palang and Martin, For the published analysis; do you have any information on the covariates that you would like to investigate? (mean and sd or range). Another factor weighting in the approach you take may be what functional form(s) you consider for continuous covariates (e.g. Linear vs. power). If you have the means for the previous analysis then one simple solution may be to centre any investigated covariates around these (prior) covariate means. If you find any highly important covariates, you may additionally consider a lower omega on that parameter since the prior did not take this covariate into account. (with a linear cov model and in the simplest case: based on covariate sd in the previous study and the estimated covariate coefficient - this correction could be implemented on the fly, but is only important if you study pop has any very important cov effects beyond the allometry correction). Best regards Jakob Skickat från min iPhone 22 jun 2012 kl. 19:39 skrev "Palang Chotsiri" <[email protected]>: > Dear NMusers, > > I am trying to model a sparse dataset by using the benefit of previously > published parameter estimates (based on rich data sampling). When applying > the $PRIOR subroutine, the THETAs and ETAs estimates of the new dataset are > reasonable and the model fit satisfactory. > > My question now relates to covariate modeling when a prior is applied. No > significant covariate relationships are included in my prior model (apart > from allometric scaling). The prior was derived based on rich PK sampling but > a fairly small sample size. The later sparse sampling study is conducted in a > larger group compare to the previous study. This might render us a greater > power to detect covariate relationships based on this dataset. > > Or problem lies in that we do not know how we can correctly conduct a > covariate model search with this model? The parameter estimates of the prior > are conditioned on the covariate distribution in the dataset on which it was > derived and are not necessarily relevant when a covariate relationship is > included. > > Perhaps there is no ideal solution but we would be grateful for any ideas on > how to best conduct covariate model building when a prior is used. > > Best regards, > Palang Chotsiri & Martin Bergstrand > > Mahidol-Oxford Tropical Medicine Research Unit, > Bangkok 10400, THAILAND > > > Ps. Ideal is of course to model both datasets together but that might not > always be possible for practical reasons.