TABLE 4. Comparison of goodness-of-fit and classification accuracy between models.

Performance metric	Base model	Model 1	Model 2	Model 3	Model 4	Model 5	Model 6
Goodness-of-fit, multinomial models
–2 Log-likelihood	1842.98	1455.33	1762.55	1835.90	1711.69	1833.55	1429.88
BIC	1998.64	1625.13	1932.33	2005.70	1881.49	2003.35	1656.28
Classification accuracy, multinomial models
PBCARa	0.50	0.50	0.50	0.50	0.50	0.50	0.50
Model classification accuracy	0.67	0.72	0.66	0.67	0.69	0.67	0.71
% improvement over PBCAR	34.0	44.0	32.0	34.0	38.0	34.0	42.0
Logistic regression comparing persistently high-cost with persistently low-cost (n = 873)
c statistic	0.74	0.87	0.80	0.75	0.85	0.76	0.88
Logistic regression comparing occasionally high-cost with persistently low-cost (n = 1082)
c statistic	0.68	0.82	0.73	0.69	0.74	0.68	0.83

Notes: Base model = age, sex, residence, Charlson comorbidity index; Model 1 = base model + no. of hospital admission in index episode; Model 2 = base model + no. of emergency department visits in index episode; Model 3 = base model + no. of family practitioner visits in index episode; Model 4 = base model + no. of specialist visits in index episode; Model 5 = base model + no. of drugs dispensed in index episode; Model 6 = base model + no. of all the above healthcare services in index episode. BIC, Bayesian information criterion; PBCAR, proportional-by-chance accuracy rate.

^aPBCAR = (100/1182)² + (309/1182)² + (773/1182)² = 0.50.