Table 2.

Fit statistics of hierarchical logistic models predicting vena cava filter use

	Model 1: Random effects only	Model 1 + level 1 fixed effects	Model 1 + level-2 fixed effects	Full model: Model 1 + level 1 and level-2 fixed effects a	Cancer model b
Intercept (SE)	−2.70 (0.09)	−5.76 (0.18)	−3.38 (0.20)	−6.50 (0.26)	−7.10 (0.86)
Hospital random effects, τ (SE)	0.45 (0.10)	0.48 (0.11)	0.22 (0.05)	0.25 (0.06)	0.12 (0.04)
ICCc	12.0%	12.7%	6.4%	7.1%	3.5%
C-statistic	0.62	0.81	0.62	0.81	0.81
AICd	50326.46	42570.91	50301.07	42548.15	–
BICd	50330.61	42670.58	50315.61	42658.19	–

^aLevel 1 fixed effects are patient level fixed effects including all demographic and clinical characteristics. Level-2 fixed effects are hospital characteristics.

^bCancer only model included only individuals with cancer and the individual sites of cancer (Table 4). Fit statistics are not included since it was not compared to other models.

^cIntraclass correlation coefficient: The proportion of the model variance explained by the “hospital” parameter; e.g. 12.0% of the Model 1 variance is explained by the hospital where a person is discharged. Calculated by τ/τ+3.29 for a binary logit model. All ICC values between hospitals were significant at p<0.001.

^dAkaike information criterion and Bayesian information criterion fit statistics for comparison between models. Each measures the model fit but penalizes for additional parameters added to each model. Smaller values are preferred; thus, the full model is preferred over Model 1.