The extracted sets of clinical variables were analysed with multivariate logistic regression analysis, fitting new regression coefficients.
A multivariate analysis was fit for the duration of hospitalization after the initial BSE (Table 3).
For the multivariate analysis we fitted several multiple logistic regression models by applying a combination of forward stepwise logistic regression and purposeful selection of variables.
At the first step of multivariate analysis, we fitted a full multinomial logistic regression model with age, gender, underlying comorbidity, exposure score, initial rheumatic involvement and CHIKV-specific IgG titres as explanatory variables, and relapsing or lingering RMSP as outcome variables with recovery as the referent category.
The seven characteristics of the ith grain are denoted as the vector x i, and the class (kanuka = 0, manuka = 1) as the scalar y i. Linear discriminant analysis fits two multivariate Gaussian distributions to the x i, one, with density f1 x), to the points with y i = 1, and the other, f0 x) to the points with y i = 0.
In this multivariate analysis, model fitness was checked by Hosmer-Lemeshow goodness-of-fit test.
Multivariate analysis was carried out fitting a forward step-up model.
Multivariate analysis was performed by fitting a Cox proportional hazard model to the data.
Multivariate analysis was conducted by fitting logistic regression analysis models to adjust for the effects of confounding factors while identifying significant predictors of DVT.
Finally, a multivariate analysis was done by fitting the logistic regression model to identify factors associated with intention to initially practice medical profession in rural areas and perceptions of emigration by controlling for the effect of potential confounding variables such as age, sex, religion, rural urban background, parental education and sources of family's income.
