Table 3.

Multivariate analysis identifying factors associated with SVR in 91 patients with CHC receiving the combination therapy

	Model 1		Model 2		Model 3
	Coefficient	P value	Coefficient	P value	Coefficient	P value
Intercept	12.4662	0.0133	12.4944	0.0130	14.5007	0.0014
Clearance rate	0.3827	0.1098	0.3897	0.0975	0.4541	0.0453
Log10 (production rate)	−1.0786	0.0179	−1.0840	0.0171	−1.2576	0.0024
rs8099917	−2.6815	0.0144	−2.5965	0.0045	−2.6630	0.0029
HCV genotype	0.6936	0.4423	0.7511	0.3591
rs8099917 × HCV genotype	0.2548	0.8856
AIC	72.194		70.215		69.097

SVR was defined as undetectable serum HCV RNA 24 wk after discontinuation of treatment, and we used 34 IU/mL as the undetectable HCV RNA level. The logistic regressions for SVR among different models are shown in this table. Multivariate analysis with logistic regression modeling was performed to examine the association and interaction between various factors and SVR. SVR was used as the dependent variable, and SNP rs8099917, HCV genotype, virion clearance rate, production rate, and interaction term of SNP rs8099917 and HCV genotype were used as independent variables in model 1; SNP rs8099917, HCV genotype, virion clearance rate, and production rate were used as independent variables in model 2; and virion clearance rate, production rate, and SNP rs8099917 were used as independent variables in model 3. According to model selection by the Akaike Information Criterion (AIC), model 3 would be the most appropriate model for SVR. The patients with a higher clearance rate, lower production rate, and TT genotype would be more likely to achieve an SVR. Otherwise, the virus genotype could be a latent factor influencing the kinetic parameters, or its effect could be shared by the kinetic parameters.