Different ODE models of tumor growth can deliver similar results

BMC Cancer

Table 2 Summary statistics relating to growth curves models fit to the treated data

model	-2*LL	AIC	w(AIC)	AIC_c	w(AIC_c)	BIC	w(BIC)
Simeoni -1	6786.12	6818.12	.015	6818.91	.016	6833.23	.020
Simeoni - 2	6780.10	6812.10	.310	6812.89	.325	6827.21	.410
Simeoni - 3	6780.32	6812.32	.278	6813.11	.291	6827.44	.366
delay	6775.61	6811.61	.397	6812.64	.368	6828.61	.204

Notes
1, 2, and 3 in the Simeoni model designations refer to the number of delay compartments incorporated into these models (Fig. 1). The delay model has one peripheral compartment, with an explicit delay in elimination (Fig. 2)
LL log likelihood, AIC Akaike information criterion, w(AIC) weights derived from candidate model AIC values, AIC_c corrected Akaike information criterion, w(AIC_c) weights derived from candidate model AIC_c values, BIC Bayesian information criteron, w(BIC) weights derived from candidate model BIC values

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