  In cancer epidemiology using population-based data, regression models for the
excess mortality hazard is a useful method to estimate cancer survival and to
describe the association between prognosis factors and excess mortality. This
method requires expected mortality rates from general population life tables:
each cancer patient is assigned an expected (background) mortality rate
obtained from the life tables, typically at least according to their age and
sex, from the population they belong to. However, those life tables may be
insufficiently stratified, as some characteristics such as deprivation,
ethnicity, and comorbidities, are not available in the life tables for a number
of countries. This may affect the background mortality rate allocated to each
patient, and it has been shown that not including relevant information for
assigning an expected mortality rate to each patient induces a bias in the
estimation of the regression parameters of the excess hazard model. We propose
two parametric corrections in excess hazard regression models, including a
single-parameter or a random effect (frailty), to account for possible
mismatches in the life table and thus misspecification of the background
mortality rate. In an extensive simulation study, the good statistical
performance of the proposed approach is demonstrated, and we illustrate their
use on real population-based data of lung cancer patients. We present
conditions and limitations of these methods, and provide some recommendations
for their use in practice.
