Excess Hazard Modelling Considering Inappropriate Mortality Rates
xhaz function
Akaike's Information Criterion for excess hazard model with baseline h...
Akaike's Information Criterion for excess hazard model with baseline h...
Akaike's Information Criterion for excess hazard model from mexhazLT f...
anova.bsplines function used for likelihood-ratio Test of two models f...
anova.constant function used for likelihood-ratio Test of two models f...
anova.mexhazLT function used for likelihood-ratio Test of two models f...
Bayesian Information Criterion for excess hazard model with baseline h...
Bayesian Information Criterion for excess hazard model with baseline h...
Bayesian Information Criterion for excess hazard model from mexhazLT f...
duplicate function
exphaz function
mexhazLT function
plot.bsplines
plots of excess hazard and net Survival from an predxhaz object
Predictions of excess hazard and net Survival from a bsplinesobject
Predictions of excess hazard and net Survival from an constantobject
A print.bsplines Function used to print a object of class bsplines
A print.constant Function used to print a object of class constant
A print.predxhaz Function used to print a object of class predxhaz
qbs function
A summary.bsplines Function used to print a object of class bsplines
A summary.constant Function used to print a object of class `xhaz.cons...
Excess Hazard Modelling Considering Inappropriate Mortality Rates
Fits relative survival regression models with or without proportional excess hazards and with the additional possibility to correct for background mortality by one or more parameter(s). These models are relevant when the observed mortality in the studied group is not comparable to that of the general population or in population-based studies where the available life tables used for net survival estimation are insufficiently stratified. In the latter case, the proposed model by Touraine et al. (2020) <doi:10.1177/0962280218823234> can be used. The user can also fit a model that relaxes the proportional expected hazards assumption considered in the Touraine et al. excess hazard model. This extension was proposed by Mba et al. (2020) <doi:10.1186/s12874-020-01139-z> to allow non-proportional effects of the additional variable on the general population mortality. In non-population-based studies, researchers can identify non-comparability source of bias in terms of expected mortality of selected individuals. An excess hazard model correcting this selection bias is presented in Goungounga et al. (2019) <doi:10.1186/s12874-019-0747-3>. This class of model with a random effect at the cluster level on excess hazard is presented in Goungounga et al. (2023) <doi:10.1002/bimj.202100210>.