In particular, two different treatment variables are considered.
We consider binary treatments by applying thresholds to the examined treatment variables.
Descriptive statistics for the control and democracy treatment variables are provided in Table 1.
ANOVA can also be used to study two or more treatment variables simultaneously.
Among the treatment variables tested, extent of resection appears to be the most consistent prognostic factor.
RSM is a valuable tool used to determine the optimum levels of two or more treatment variables.
Descriptive statistics of the treatment variables and randomly assigned resume characteristics are also provided in Table 2.
Multiple demographic, clinical, and treatment variables were collected and analyzed with univariate analysis followed by multivariate analysis.
We also use a panel semi-parametric model to estimate the dose response function for continuous treatment variables.
In silico studies have the advantage that treatment variables can be controlled.
The Fisher's exact test was used to test the independence between age and tumour and treatment variables (dichotomized into extremes).
