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Excess hazard modeling is one of the main tools in population-based cancersurvival research. Indeed, this setting allows for direct modeling of thesurvival due to cancer even in the absence of reliable information on the causeof death, which is common in population-based cancer epidemiology studies. Wepropose a unifying link-based additive modeling framework for the excess hazardthat allows for the inclusion of many types of covariate effects, includingspatial and time-dependent effects, using any type of smoother, such as thinplate, cubic splines, tensor products and Markov random fields. In addition,this framework accounts for all types of censoring as well as left-truncation.Estimation is conducted by using an efficient and stable penalizedlikelihood-based algorithm whose empirical performance is evaluated throughextensive simulation studies. Some theoretical and asymptotic results arediscussed. Two case studies are presented using population-based cancer datafrom patients diagnosed with breast (female), colon and lung cancers inEngland. The results support the presence of non-linear and time-dependenteffects as well as spatial variation. The proposed approach is available in theR package GJRM.

A Unifying Framework for Flexible Excess Hazard Modeling with Applications in Cancer Epidemiology