This model used gender and age-specific sexual behavior characteristics (e.g., partner acquisition rates, mixing between age groups) as risk factors for HPV infection.
Multivariate models using gender, age, ethnic origin, residence, site of infection, status (new or relapse), drug resistance, BCG status, chest x-ray and cavity were analyzed and potential interactions were assessed for significance.
The second table presents the quality of language modeling using gender-dependent acoustic models (optimized to male and female speech) trained on the APD1, APD2 and PAR databases, giving the best results in previous experiment.
The association between a more reduced E h CyS and DD was maintained on a multivariate analysis (model 2) using gender, BMI, and oxidative stress measures as predictive variables (adjusted OR: 1.22; 95% CI: 1.08 1.37).
The survival model was fit using gender, baseline CD4 cell count, and age.
To assess and adjust for potential bias in the sample that attended the clinic, a logistic model was derived using gender, social class, maternal education and ethnicity.
Tables 1, 2, 3 summarize the association of β-AR genotypes with HRV indices determined by a GLM model using age, gender, and BMI as covariates.
Three models were developed to predict death up to 7-days post-discharge: 1/a simple background risk model using age, gender; 2/a time-varying risk model for exposure to hospitalization (admission time, days in hospital); 3/ disease specific models (Charlson co-morbidity index, DRG).
We opted for the "product method" 16 of estimating the indirect effect which required the construction of a linear regression model for baseline CD4 and an accelerated failure time model with a Weibull distribution using gender, baseline CD4 cell count, and age.
The "product method" of estimating the indirect effect requires the construction of a linear regression model for baseline CD4 cell count and an accelerated failure time model with a Weibull distribution using gender, baseline CD4 cell count and age.
