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| 縦断データとイベント発生までの時間データの同時モデル× | 条件付き生存および動的予測のためのランドマーク解析× | |
|---|---|---|
| 分野 | 生存時間解析 | 生存時間解析 |
| 系統 | Survival analysis | Survival analysis |
| 提唱年≠ | 2004 | 1983 |
| 提唱者≠ | Tsiatis, A.A. & Davidian, M.; Rizopoulos, D. | Anderson, J. R., Cain, K. C. & Gelber, R. D. |
| 種類≠ | Semiparametric regression model | Conditional survival estimator |
| 原典≠ | Rizopoulos, D. (2012). Joint Models for Longitudinal and Time-to-Event Data. CRC Press. DOI ↗ | 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 ↗ |
| 別名 | joint model, shared random effects model, longitudinal-survival joint model, Joint Model (Boylamsal + Sağkalım Birleşik Model) | landmark method, dynamic prediction, conditional survival estimation, Landmark Analizi (Dinamik Tahmin) |
| 関連≠ | 5 | 3 |
| 概要≠ | 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. | 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. |
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