RE: estimating Ka from dataset combining rich sample study and sparse sampling study

Hi Ethan, If OMEGA(?) for KA is drastically reduced when including the sparse data, then something is wrong with your model and in this case it is not the estimation method or assumption on distribution of individual parameter). Eta-shrinkage would not drastically reduce the estimate of OMEGA, since this estimate is driven by the subjects/studies which contain information on the parameter. If the sparse data is multiple dosing it may be that KA is variable between occasions, rather than between subjects (assuming the sparse data contain some information on KA). Or if the sparse data is from a less well-controlled study or a different population, it may be that increased IIV in other parts of the model (e.g. OMEGA on V) is making IIV in KA appear low for the rich study, when fitting the two studies together. If you get the covariate model in place this problem will be solved. For the simple model you have it should be quick to start out assuming that most parameters (THETAs and OMEGAs) are different between the two studies and then reduce down to a model which is stable and parsimonious. Obviously, if you eventually can explain the differences using more mechanistic covariates than study number that is of more use. Cheers Jakob