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 -----Original Message----- From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On Behalf Of Palang Chotsiri Sent: 22 June 2012 19:13 To: nmusers@globomaxnm.com Subject: [NMusers] 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.
