Modeling excess mortality comparing with a control population: A combined additive and relative hazards model

In this presentation, I propose a flexible parametric excess hazard model on the log-hazard scale, incorporating a modeled expected rate from a control population (for example, matched comparators). Covariate effects are assumed to be multiplicative within both the expected hazard and the excess hazard, while the presence of disease among the studied group has an additive effect, hence the excess hazard. By modeling the expected rate, we can appropriately allow for uncertainty. The model is extended to include time-dependent effects, multiple time scales, and more. Following estimation, we quantify results through the prediction of the survival, hazard, and cumulative incidence functions, as well as transformations of these, and crucially with associated conKdence intervals on all measures. The proposed method has been implemented in the Stata package stexcess (github.com/RedDoorAnalytics/stexcess).