On models for the estimation of the excess mortality hazard in case of insufficiently stratified life tables

Abstract

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.

Age disparities in lung cancer survival in New Zealand: the role of patient and clinical factors

“…However, in the case of lung cancer, patients are more likely to smoke and have higher risk of mortality than the general population. Studies showed that lung cancer mortality hazard was overestimated, and consequently, lung cancer net survival was underestimated when not using smoking-specific life-tables 33,34 . In New Zealand, data on smoking status is collected every 7 years during population census.…”

The impact of timely cancer diagnosis on age disparities in colon cancer survival in New Zealand

“…However, this information was unavailable at population level. Consequently, the estimation of excess mortality hazards may be overestimated, and therefore our net survival estimates underestimated 64 .…”

Understanding disparities in cancer prognosis: An extension of mediation analysis to the relative survival framework

“…Causal interpretation is only appropriate when lifetables are sufficiently stratified, but in principle lifetables can be constructed for any number of factors. To deal with this issue and consider other risk factors that are not always available on a population level, adjustments at the expected mortality rates have been suggested (Bower et al., 2017; Ellis et al., 2014; Rubio et al., 2019). Finally, we have assumed no intermediate confounders, that is no mediator–outcome confounder affected by the exposure (cross‐world independence assumption).…”

“…If the variables of the lifetables are insufficient then this assumption is violated. Various methods have been suggested to incorporate factors that are not available on population level (Bower et al., 2017; Ellis, Coleman, & Rachet, 2014; Rubio, Rachet, Giorgi, Maringe, & Belot, 2019).…”