A semi-Markov model for multistate and interval-censored data with multiple terminal events. Application in renal transplantation.
The semi-Markov assumption emphasizes the importance of time spent in a state. In order to compute this type of multistate model, most transition times are always considered to be exactly identified or right censored. However, in the longitudinal analysis of chronic diseases, investigators are often confronted with interval-censored data (transition times are known to have occurred in some interval). Thus, the two key issues are the modeling of the duration dependence and the interval censoring. In this article, we define a semi-Markov model, allowing for interval censoring, for parametric hazard functions with a union or logical sum- or intersection-shape and for determination of initial states according to covariates. Our modeling approach is specific to each transition, so as to obtain a more coherent model. Parallel to competing risks models, the multistate model takes into account several final events. We consider an example of kidney transplant recipient follow-up to illustrate the utility of the method.
Foucher
Yohann
Y
Laboratoire de Biostatistique, Institut Universitaire de Recherche Clinique, 641 av. du doyen Gaston Giraud, 34093 Montpellier, France. yohann.foucher@iurc.montp.inserm.fr
Giral
Magali
M
Soulillou
Jean-Paul
JP
Daures
Jean-Pierre
JP
D000367:Age Factors; D015415:Biomarkers / Q000652:urine; D003404:Creatinine / Q000652:urine; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D018450:Disease Progression; D006801:Humans; D016030:Kidney Transplantation / Q000473:pathology / Q000706:statistics & numerical data; D016013:Likelihood Functions; D008137:Longitudinal Studies; D008390:Markov Chains; D016016:Proportional Hazards Models; D011507:Proteinuria / Q000652:urine; D012306:Risk; D012720:Severity of Illness Index; D012737:Sex Factors; D016019:Survival Analysis; D013997:Time Factors; D035501:Uncertainty
D015415:Biomarkers; D003404:Creatinine