In isolating risk factors significantly associated with poor outcomes at EOS among cures at EOT, only age ≥50 and baseline BMI <18.5 were identified (Table 6), suggesting that once a patient is cured, many traditional baseline predictive variables, such as chest x-ray status, previous TB treatment history, and MDR-TB status may no longer be as relevant for prognosis.
Many of these potential predictive variables are correlated with each other and there are arguments for and against models examining the effect of socio-economic variables with or without allowance for the respiratory function of the patients and their age.
After excluding influential observations, the model selection for DDT and DDE included many of the same predictive variables as the original models (see Supplemental Material, Tables S3 and S4).
Many in-hospital mortality risk prediction scores dichotomize predictive variables to simplify the score calculation.
Nevertheless, statistical techniques always rely on the assumptions on linear separability, multivariate normality, and independence of the predictive variables; unfortunately, many of the common models of treating the financial markets problems violate these assumptions.
Predictive variables explain 24% of variability in results of depression.
In contrast to many predictive validation studies [ 9, 14, 20], the predictor and outcome variables used in this study were not school-dependent, and therefore, we were able to conduct a single analysis for the entire national population.
Stepwise multiple regression was used for selecting significant predictive variables.
Disease networks were used as response variables and Drug networks were used as predictive variables.
Most predictive variables were categorized a priori.
