title: Estimating net survival: the importance of allowing for informative censoring.
abstract: Net survival, the one that would be observed if cancer were the only cause of death, is the most appropriate indicator to compare cancer mortality between areas or countries. Several parametric and non-parametric methods have been developed to estimate net survival, particularly when the cause of death is unknown. These methods are based either on the relative survival ratio or on the additive excess hazard model, the latter using the general population mortality hazard to estimate the excess mortality hazard (the hazard related to net survival). The present work used simulations to compare estimator abilities to estimate net survival in different settings such as the presence/absence of an age effect on the excess mortality hazard or on the potential time of follow-up, knowing that this covariate has an effect on the general population mortality hazard too. It showed that when age affected the excess mortality hazard, most estimators, including specific survival, were biased. Only two estimators were appropriate to estimate net survival. The first is based on a multivariable excess hazard model that includes age as covariate. The second is non-parametric and is based on the inverse probability weighting. These estimators take differently into account the informative censoring induced by the expected mortality process. The former offers great flexibility whereas the latter requires neither the assumption of a specific distribution nor a model-building strategy. Because of its simplicity and availability in commonly used software, the nonparametric estimator should be considered by cancer registries for population-based studies.
authors.affiliation: Hospices Civils de Lyon, Service de Biostatistique, F-69003, Lyon, France. coraline.danieli@chu-lyon.fr
authors.forename: Coraline
authors.initials: C
authors.lastname: Danieli
authors.forename: Laurent
authors.initials: L
authors.lastname: Remontet
authors.forename: Nadine
authors.initials: N
authors.lastname: Bossard
authors.forename: Laurent
authors.initials: L
authors.lastname: Roche
authors.forename: Aurélien
authors.initials: A
authors.lastname: Belot
mesh_terms: D002423:Cause of Death; D003198:Computer Simulation; D006801:Humans; D015233:Models, Statistical; D016019:Survival Analysis