Tamara van Donge

Dynamics of creatinine in ELBW neonates 145 7 Thereby, three parameters have to be estimated: • the fixed effect vector: !" !" ϕ # !" ! $ Ω . • the random effect parameter quantifying the residual unknown variability: σ 2 • the random effect parameter quantifying the inter-individual variability: !" !" ϕ # !" ! $ Ω . Numerical criteria to evaluate the predictive performance of development model Evaluation analysis was performed to evaluate the developed creatinine model. The mean percentage error (MPE), relative MPE (RMPE), mean squared error (MSE) and the relative root mean squared error (RMSE) were calculated to evaluate the accuracy and precision of the model predictions. 1. Prediction error (PE) = ! – ! "#$% ! – ()* ! ()* ! × 100 + , ∑( ! ) + , ∑( % ! ) + , ∑( ! ) - 2 + , ∑( % ! ) - 2. Relative prediction error (PE%) = ! – ! "#$% ! – ()* ! ()* ! × 100 + , ∑( ! ) + , ∑( % ! ) + , ∑( ! ) - 2 + , ∑( % ! ) - 3. Mean prediction error (MPE) = ! – ! "#$% ! – ()* ! ()* ! × 100 + , ∑( ! ) + , ∑( % ! ) + , ∑( ! ) - 2 + , ∑( % ! ) - 4. Relative MPE = ! – ! "#$% ! – ()* ! ()* ! × 100 + , ∑( ! ) + , ∑( % ! ) + , ∑( ! ) - 2 + , ∑( % ! ) - 5. Mean squared error (MSE) = ! – ! "#$% ! – ()* ! ()* ! × 100 + , ∑( ! ) + , ∑( % ! ) + , ∑( ! ) - 2 + , ∑( % ! ) - 6. Relative MSE (RMSE) = ! – ! "#$% ! – ()* ! ()* ! × 100 ! ) % ! ) + , ∑( ! ) - 2 + , ∑( % ! ) -