Model assumption misspecification

Box (1976), in one of the most famous quotes reported in the pharmacokinetic literature, stated all models are wrong, some are useful. This adage is well accepted. The question then becomes how precise or of what value are the parameter estimates if the model or the model assumptions are wrong. A variety of simulation studies have indicated that population parameter estimates are surprisingly robust to all kinds of misspe-cifications, but that the variance components are far more sensitive and are often very biased when misspeci-fication of the model or when violations of the model assumptions occur. Some of the more conclusive studies examining the effect of model misspecification or model assumption violations on parameter estimation will now be discussed. 

Because of its many assumptions, a population model, especially with all the pre-specification demanded in this framework, is unlikely to be true. However, one can argue that this framework exerts the influence of model misspecification primarily on study power. This is because a misspecified model would generally result in lower power although not larger Type I error. In addition, this approach maintains a more realistic confidence interval width instead of an overly optimistic (short) one. By maximizing the model as much as data can be expected to support, the impact on Type I error is minimized. Therefore, the hypothesis test is made as conservative as possible, and thus suitable for BE assessment. 

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