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Correlation analyses showed that no colinearity was prevalent between the independent variables, an assumption for the logistic regression.
The assumptions of the model will be tested and reanalysed using a Q-Q plot diagram for the assumption of normality, and by comparing the studentised residuals versus the values of the independent variable for the assumption of constant variances.
We evaluated each independent variable for the assumption of proportional hazards.
Independent variable.
In our case, we have X 0 R ( t ), X 1 R ( t ), X 2 R ( t ), …, X M - 1 R ( t ) is a sequence of independent random variables (Assumption 1).
We also use DOTS treatment success rate as an independent variable, based on the assumption that higher success rates indicate higher programme quality on the ground.
Levene's Test of Equality of Error variances was not significant for any independent variable, thus verifying the assumption of homoschedasticity of variances.
Proportional hazard assumption was assessed and all the independent variables satisfied the assumption.
We examined residual diagnostics to investigate deviations from standard linear mixed-model assumptions (functional form of independent variables and covariance assumptions) and the presence of influential observations.
In cases where the coefficient was equal to 0 between an independent variable and a dependent one, the assumption was that for the dependent value there was no correlation and hence it was to be excluded from the model.
A Wald test by Brant [37] was performed to test the parallel regression assumption for each independent variable considered in the ORM [38].
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