The levels of leptin and estrogen were added to regression models as dependent variables, respectively; disease status (aMCI or normal control) was independent variable, and age and BMI were adjusted.
In this simplified example, the GRN system has 3 genes corresponding to the model variables x1, x2, x3, respectively.
Hence, we varied the values describing treatment success in our trust model (variables e and s, respectively) over the 20-80% range (Table 1).
In the multiple linear regression analysis, total adjusted R for the model and changes in R for the independent contribution of single predictors will be calculated to assess the total variance in the outcome variable accounted for by the whole model and single explanatory variables, respectively.
In multivariate analyses, UAPI (⩽1 vs >1) was an independent predictor of MTX-R with odds ratios (ORs) of 2.82 (P<0.0001) or 2.82 (P<0.0001 relative to the CXH score (model A) or FIGO score (model C) considered as continuous variables, respectively (Table 3).
This result changed little when the change was considered only for the five variables with most independent explanatory power (77.5%±5.86, 90.5%±5.04, 91.0%±3.76 for models with ten, eleven and twelve variables respectively).
Using data from a longitudinal community-based study, we fitted structural equation models including two latent variables (respectively baseline adiposity and adiposity change after 2 years of follow-up), each being defined, by the four following anthropometric measurement (respectively by their changes): body mass index, waist circumference, skinfold thickness and percent body fat.
However, SVM only needed four variables to make its prediction in both models, whereas MLR needed seven and eight variables, respectively, in the first and second models.
Specifically, since this variable composed of 8 10 classes (depending on the population analyzed), its inclusion in a logistic regression model requires generating 7 9 indicator variables, respectively.
Table 3-Modle C and D included only those variables respectively from model A and B that reached a P value of <0 · 05.
