方法对比
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| 纵向数据与生存时间联合模型× | 含时变协变量的Cox回归× | |
|---|---|---|
| 领域 | 生存分析 | 生存分析 |
| 方法族 | Survival analysis | Survival analysis |
| 起源年份≠ | 2004 | 1972 |
| 提出者≠ | Tsiatis, A.A. & Davidian, M.; Rizopoulos, D. | Cox, D. R. (extended formulation by Therneau & Grambsch) |
| 类型≠ | Semiparametric regression model | Semi-parametric hazard regression model |
| 开创性文献≠ | Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press. DOI ↗ | Therneau, T. M. & Grambsch, P. M. (2000). Modeling Survival Data: Extending the Cox Model. Springer. DOI ↗ |
| 别名≠ | joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model) | time-varying covariate Cox model, extended Cox model, Zamana Bağlı Kovaryatlı Cox Regresyonu |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. | Time-dependent Cox regression is an extension of the standard Cox proportional hazards model, introduced through the counting-process formulation developed by Therneau and Grambsch (2000), that allows one or more predictor variables to take different values at different points in a subject's follow-up period. It is the method of choice whenever a covariate — such as a laboratory measurement, a medication dose, or a disease severity score — changes over time rather than remaining fixed from study entry. |
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