![]() ![]() Ten published pharmacokinetic and pharmacodynamic models as well as stochastic simulation and estimation were used to evaluate the two approaches. In this work, two strategies are proposed to extend and unify residual error modeling: a dynamic transform-both-sides approach combined with a power error model (dTBS) capable of handling skewed and/or heteroscedastic residuals, and a t-distributed residual error model allowing for symmetric heavy tails. ![]() The choice of error model is mostly done on a case-by-case basis from a limited set of commonly used models. Violations of this assumption can cause bias in parameter estimates, invalidate the likelihood ratio test and preclude simulation of real-life like data. ![]() The properties of these estimators depend on the assumption that residual errors are independent and normally distributed with mean zero and correctly defined variance. Nonlinear mixed effects models parameters are commonly estimated using maximum likelihood. ![]()
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