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Exact(33)
Introducing covariates, z, into equation (7) gives (10) This is a proportional excess hazards model.
The excess hazards for age and deprivation remained proportionate by time since randomisation.
Non-proportional excess hazards were incorporated through time-dependent effects (3 df for each spline term).
We found no difference in the excess hazards ratios when using either interval (data not shown).
Excess Hazards Ratios (EHR) at five years were computed using a generalised linear model with a Poisson error structure [ 24].
It can also incorporate continuous covariates, so the effect of age is captured in more detail, and allow for non-proportional excess hazards.
Similar(27)
The excess hazard method allows estimating an excess mortality hazard using the population (expected) mortality hazard.
The reason for modelling on the log cumulative excess hazard scale instead of the log excess hazard scale is because the log cumulative excess hazard is a relatively stable function, and it is easier to capture its shape.
When cure is reached the excess hazard rate is zero, and the cumulative excess hazard will be constant after this time.
The flexible parametric survival model [ 14, 15] is fitted on the log cumulative excess hazard scale, using restricted cubic splines to estimate the baseline cumulative excess hazard.
The latter models are fitted on the log cumulative excess hazard scale using restricted cubic splines [ 12] for the baseline excess hazard and for any time-dependent effects.
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