方法对比
并排查看您选择的方法；存在差异的行会高亮显示。
| 基于陆标的条件生存与动态预测分析× | 纵向数据与生存时间联合模型× | |
|---|---|---|
| 领域 | 生存分析 | 生存分析 |
| 方法族 | Survival analysis | Survival analysis |
| 起源年份≠ | 1983 | 2004 |
| 提出者≠ | Anderson, J. R., Cain, K. C. & Gelber, R. D. | Tsiatis, A.A. & Davidian, M.; Rizopoulos, D. |
| 类型≠ | Conditional survival estimator | Semiparametric regression model |
| 开创性文献≠ | Anderson, J. R., Cain, K. C. & Gelber, R. D. (1983). Analysis of Survival by Tumor Response. Journal of Clinical Oncology, 1(11), 710–719. DOI ↗ | Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press. DOI ↗ |
| 别名 | landmark method, dynamic prediction, conditional survival estimation, Landmark Analizi (Dinamik Tahmin) | joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model) |
| 相关≠ | 3 | 5 |
| 摘要≠ | Landmark analysis, introduced by Anderson, Cain, and Gelber in 1983, estimates conditional survival probabilities for subjects who are still at risk at a pre-specified point in time — the landmark — rather than at study entry. It was developed explicitly to avoid immortal time bias that arises when subjects are grouped by an event (such as a treatment change or biomarker result) that can only occur if they remain event-free long enough to experience it. | 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. |
| ScholarGate数据集 ↗ |
|
|