RE: OFV or Diagnostic Plot ?? Which one rules...

Hi Sumeet, OFV is an objective fit of the model to the data, so generally that should be your leading criteria. Outside of that you often have to deal with subjectivity. However, there are quite a few caveats to relying too strongly on OFV. You should try to avoid "chasing OFV" by testing too many models or those in which the theoretical justification is lacking. Which error model best agrees with other information you have about the concentrations you have? You might also consider whether poor diagnostics you see as part of the residual error model really originate from structural model-misspecification. It can happen that you "hide" the shortcomings of the structural model by putting too much flexibility into the residual error model. It then becomes very hard to improve the structural model when the information is "swallowed up" by the residual error model. You cant fix what you cant see. I often try to think about how the model will be used outside of the development process. In its intended application does the model need to predict high concentrations or low concentrations more accurately? A proportional error model lets low concentrations play a stronger role in the model likelihood compared to proportional+additive. Basically, getting OFV 20 points lower with prop+add compared to prop means that the model can fit higher concentrations better if a little bit worse fit can be tolerated in low concentrations. You have to decide which one is most appropriate. It depends on how the model is intended to be used and how your structural model compares to what you think the "true" model might be. Warm regards, Douglas Eleveld