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A semi-Markov model for multistate and interval-censored data with multiple terminal events. Application in renal transplantation.
Stat Med 2007; 26(30):5381-93SM

Abstract

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.

Authors+Show Affiliations

Laboratoire de Biostatistique, Institut Universitaire de Recherche Clinique, 641 av. du doyen Gaston Giraud, 34093 Montpellier, France. yohann.foucher@iurc.montp.inserm.frNo affiliation info availableNo affiliation info availableNo affiliation info available

Pub Type(s)

Journal Article

Language

eng

PubMed ID

17987670

Citation

Foucher, Yohann, et al. "A semi-Markov Model for Multistate and Interval-censored Data With Multiple Terminal Events. Application in Renal Transplantation." Statistics in Medicine, vol. 26, no. 30, 2007, pp. 5381-93.
Foucher Y, Giral M, Soulillou JP, et al. A semi-Markov model for multistate and interval-censored data with multiple terminal events. Application in renal transplantation. Stat Med. 2007;26(30):5381-93.
Foucher, Y., Giral, M., Soulillou, J. P., & Daures, J. P. (2007). A semi-Markov model for multistate and interval-censored data with multiple terminal events. Application in renal transplantation. Statistics in Medicine, 26(30), pp. 5381-93.
Foucher Y, et al. A semi-Markov Model for Multistate and Interval-censored Data With Multiple Terminal Events. Application in Renal Transplantation. Stat Med. 2007 Dec 30;26(30):5381-93. PubMed PMID: 17987670.
* Article titles in AMA citation format should be in sentence-case
TY - JOUR T1 - A semi-Markov model for multistate and interval-censored data with multiple terminal events. Application in renal transplantation. AU - Foucher,Yohann, AU - Giral,Magali, AU - Soulillou,Jean-Paul, AU - Daures,Jean-Pierre, PY - 2007/11/8/pubmed PY - 2008/4/5/medline PY - 2007/11/8/entrez SP - 5381 EP - 93 JF - Statistics in medicine JO - Stat Med VL - 26 IS - 30 N2 - 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. SN - 0277-6715 UR - https://www.unboundmedicine.com/medline/citation/17987670/A_semi_Markov_model_for_multistate_and_interval_censored_data_with_multiple_terminal_events__Application_in_renal_transplantation_ L2 - http://www.diseaseinfosearch.org/result/7171 DB - PRIME DP - Unbound Medicine ER -