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Excess mortality is obtained by subtracting the expected mortality from the observed mortality (additive hazard model).
The additive hazard model is almost exclusively the first-choice model in population-based cancer studies.
We therefore repeated our case-complete analyses using additive hazard models [ 46].
The standard errors from the additive hazard model were smaller than those of the multiplicative model.
Goodness of fit for the additive hazard model was examined [ 27].
26 The syntax is available in Appendix 1. Calculation of excess mortality is based on an additive hazard model.
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A p-vector of the covariates Z ik in the additive hazards model acts additively on unknown baseline hazards, while it acts multiplicatively in the multiplicative model.
Some of these models are the proportional (additive) hazards model, the proportional (additive) reversed hazards model, and the proportional (additive) mean residual life model (cf. [1 6]).
The additive hazards model, which is well known in the literature, has played a prominent role in modeling survival data.
Thus, as a second lead, we use the relative hazard and the relative mean residual life orders for comparison of additive hazards model.
To eliminate the problem of heterogeneity in many practical populations, researchers have considered the unconditional survival function of the additive hazards model (cf. [7 9]).
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