title: A multistate additive relative survival semi-Markov model.
abstract: Medical researchers are often interested to investigate the relationship between explicative variables and times-to-events such as disease progression or death. Such multiple times-to-events can be studied using multistate models. For chronic diseases, it may be relevant to consider semi-Markov multistate models because the transition intensities between two clinical states more likely depend on the time already spent in the current state than on the chronological time. When the cause of death for a patient is unavailable or not totally attributable to the disease, it is not possible to specifically study the associations with the excess mortality related to the disease. Relative survival analysis allows an estimate of the net survival in the hypothetical situation where the disease would be the only possible cause of death. In this paper, we propose a semi-Markov additive relative survival (SMRS) model that combines the multistate and the relative survival approaches. The usefulness of the SMRS model is illustrated by two applications with data from a French cohort of kidney transplant recipients. Using simulated data, we also highlight the effectiveness of the SMRS model: the results tend to those obtained if the different causes of death are known.
authors.affiliation: 1 EA 4275 - SPHERE - bioStatistics, Pharmacoepidemiology and Human sciEnces REsearch team, Université de Nantes, Nantes, France.|2 INSERM CR1064 Centre pour la Recherche en Transplantation et Immunointervention (CRTI), Institut Transplantation-Urologie-Néphrologie (ITUN), Nantes, France.|3 Centre Hospitalier Universitaire de Nantes, Nantes, France.
authors.forename: Florence
authors.initials: F
authors.lastname: Gillaizeau
authors.affiliation: 1 EA 4275 - SPHERE - bioStatistics, Pharmacoepidemiology and Human sciEnces REsearch team, Université de Nantes, Nantes, France.
authors.forename: Etienne
authors.initials: E
authors.lastname: Dantan
authors.affiliation: 1 EA 4275 - SPHERE - bioStatistics, Pharmacoepidemiology and Human sciEnces REsearch team, Université de Nantes, Nantes, France.|2 INSERM CR1064 Centre pour la Recherche en Transplantation et Immunointervention (CRTI), Institut Transplantation-Urologie-Néphrologie (ITUN), Nantes, France.|3 Centre Hospitalier Universitaire de Nantes, Nantes, France.
authors.forename: Magali
authors.initials: M
authors.lastname: Giral
authors.affiliation: 1 EA 4275 - SPHERE - bioStatistics, Pharmacoepidemiology and Human sciEnces REsearch team, Université de Nantes, Nantes, France.|2 INSERM CR1064 Centre pour la Recherche en Transplantation et Immunointervention (CRTI), Institut Transplantation-Urologie-Néphrologie (ITUN), Nantes, France.|3 Centre Hospitalier Universitaire de Nantes, Nantes, France.
authors.forename: Yohann
authors.initials: Y
authors.lastname: Foucher
mesh_terms: D000328:Adult; D000368:Aged; D002423:Cause of Death; D015331:Cohort Studies; D018450:Disease Progression; D005260:Female; D005602:France; D006801:Humans; D007676:Kidney Failure, Chronic / Q000150:complications / Q000601:surgery; D016030:Kidney Transplantation; D008297:Male; D008390:Markov Chains; D008875:Middle Aged; D009369:Neoplasms / Q000150:complications; D016019:Survival Analysis
keywords: Disease progression; kidney transplantation; multistate models; relative survival; semi-Markov process; survival analysis