TABLE 3.

Model statistics after 1,000 cycles^a

Model	Parameters	AIC value	Population prediction		Individual prediction
			Bias	Imprecision	Bias	Imprecision
1	Ka, Ke, V	−2,362	0.054	6.15	−0.39	0.65
2	Ka, Ke, V, KCP, KPC	−2,435	4.81	125.28	−0.01	1.18
3	Ka, Ke, V = V0 · wt, KCP, KPC	−2,444	4.98	139.54	−0.06	0.97
4	Ka, Ke, V = V0 · BSA, KCP, KPC	−2,441	5.05	127.92	0.08	2.20
5	Ka, Ke = Ke0/wt0.25, V = V0 · wt, KCP = KCP/wt0.25, KPC = KPC/wt0.25	−2,440	3.51	108.65	−0.01	1.16

^aAll size-scaled models (models 3 to 5) are similar, but model 5, with allometric scaling for body size, is preferable based on minimization of AIC and favorably low bias and imprecision. AIC, Akaike information criterion, with the lowest value indicating the most likely model; bias, mean weighted error of predictions minus observations; BSA, body surface area in m² normalized to a mean population BSA of 1.94 m²; imprecision, bias-adjusted mean weighted squared error of predictions minus observations; K_a, absorption from dosing to the central compartment; K_CP, transfer from the central to the peripheral compartment; K_e, elimination from the central compartment; K_PC, transfer from the peripheral to the central compartment; V, volume of the central compartment; wt, weight in kg normalized to 70 kg.