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The fit of the Cox proportional hazards models was examined by plotting the hazard functions in different categories of risk factors over time.
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The proportional hazard assumption was tested graphically - by plotting the log (cumulative hazard) vs follow-up time - and formally using Schoenfeld residuals.
Figure 5 illustrates this change by plotting the piecewise constant hazard rate in each treatment arm for the COOP trial.
In the derived model, plotting the log-minus-log survival function tested the proportionality assumption (Kalbfleisch and Prentice, 1980), and the goodness of fit was judged by plotting the cumulative baseline hazard function for residuals (Kay, 1977).
The proportional hazard assumption for each of the selected variables retained in the final model was initially checked by plotting the log cumulative baseline hazard ratio.
The proportional hazard assumptions were tested by plotting the logarithm of the integrated hazards (log log survival plots).
The proportional hazard assumptions were tested by plotting the logarithm of the integrated hazards (log log survival plots) and by Schoenfeld tests, and found to be satisfied.
The hazard analysis was completed by plotting the RPNs of higher risk failure modes in a priority matrix (figure 1), which is a graph divided into four coloured areas reflecting different levels of priority for action: Area 1 (red) urgent action required; area 2 (orange) prompt action required; area 3 (yellow) scheduled action required; area 4 (green) monitoring required.
The proportional hazards assumption was investigated by plotting the natural logarithm of the cumulative hazard, from Cox regression without covariates as described above, against the log of DYAR.
We used Cox proportional hazards models to estimate the relative hazards (with 95% confidence intervals (CI)) after testing for the assumption of proportional hazards by plotting the Schoenfeld residuals.
We checked the assumption of proportional hazards by plotting the scaled Schoenfeld residuals [28].
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