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The proportionality hazard assumption, assessed using log(−log(survival function)) plots, was met for all covariates.
We found no evidence for violation of the proportional hazard assumption, assessed by testing for a non-zero slope of the scaled Schoenfeld residuals on functions of time.
Cox proportional hazards modeling (forward conditional entry at P < 0.05, removal at P > 0.05) was used to determine independent predictors of these events, with the validity of the proportional hazards assumption assessed from ln[−ln(survival)] curves and time-dependent covariates.
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Categorical variables are shown as frequencies and percentages, while continuous variables are presented as means and standard deviations or as medians and interquartile range, depending on normality assumptions (assessed by normal probability plots).
This model enables the user to conduct various simulation studies, including testing the performance of assessment models under different assumptions, assessing the impacts of model mis-specification and evaluating management strategies.
The assumption of linearity and the proportional hazards assumption were assessed using Schoenfeld and martingale residuals.
The first two assumptions were validated graphically and the independence assumption was assessed with the Durbin-Watson statistic (values obtained close to 2) [ 39].
Proportional hazard assumption was assessed and all the independent variables satisfied the assumption.
The validity of proportional hazards assumption was assessed both visually and numerically, and no marked deviation for assumptions were observed.
The error of this assumption is assessed as function of the soot porosity.
The proportional odds assumption was assessed using the Brant test.
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