In real life

Dear NM users: I have questions about the principles. It is rather common that clinical PK data are 'bad': very sparse sampling and/or the sampling stopped too early. (I understand that you can never make a useful model with 'bad' data, but in the case that you have to make a model from them). In this case, I understand that you want to go for a simpler model, say if rich data support a 3-compartment model, you probably need to go for a 2-compartment or even a 1-compartment model, otherwise you may see signs of overparameterization. However, after I modeled the data with a simpler model, I saw situations where the GOF plots are biased, with low concentration being underestimated and high concentration overestimated; the CWRES vs. PRED plot showed a falling trend line. My questions are: 1. Are these bias due to the use of a simplified model? 2. if the answer is 'yes', should I go back to a more complex model but fix some of the parameters based on literature? 3. Are these, after all, legitamate questions? or should I just say 'the data are bad, we cannot make a model from it?" Thank you very much in advance for your input. Best regards, Siwei