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| Modello Congiunto per Dati Longitudinali e Tempo all'Evento× | Modello di sopravvivenza per eventi ricorrenti× | |
|---|---|---|
| Campo | Analisi di sopravvivenza | Analisi di sopravvivenza |
| Famiglia | Survival analysis | Survival analysis |
| Anno di origine≠ | 2004 | 1981 |
| Ideatore≠ | Tsiatis, A.A. & Davidian, M.; Rizopoulos, D. | Andersen & Gill (AG, 1982); Prentice, Williams & Peterson (PWP, 1981); Wei, Lin & Weissfeld (WLW, 1989) |
| Tipo≠ | Semiparametric regression model | Semi-parametric hazard model for repeated events |
| Fonte seminale≠ | Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press. DOI ↗ | Cook, R.J. & Lawless, J.F. (2007). The Statistical Analysis of Recurrent Events. Springer. DOI ↗ |
| Alias≠ | joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model) | Tekrarlayan Olay Modeli (Recurrent Events), Andersen-Gill model, AG model, Wei-Lin-Weissfeld model |
| Correlati≠ | 5 | 4 |
| Sintesi≠ | The joint model for longitudinal and time-to-event data, formalised by Tsiatis and Davidian in 2004 and extended comprehensively by Rizopoulos in 2012, simultaneously estimates a mixed-effects model for repeatedly measured biomarkers and a survival model for the time to an event, linking the two processes through shared random effects. It resolves two major problems that simpler approaches cannot handle: informative dropout from longitudinal studies and the endogeneity of time-varying biomarkers used as covariates in a Cox model. | A recurrent event model is a survival analysis extension, formalised through the landmark contributions of Prentice, Williams and Peterson (1981), Andersen and Gill (1982), and Wei, Lin and Weissfeld (1989), that models time-to-event data when the same event — such as a hospital readmission, disease relapse, or equipment failure — can occur multiple times in the same individual. The three principal frameworks are the Andersen-Gill (AG) model, the Prentice-Williams-Peterson (PWP) stratified model, and the Wei-Lin-Weissfeld (WLW) marginal model, each making different assumptions about within-subject dependence. |
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