时间标签：数据日期 2026-11-03 · 生成时间 2026-11-03 05:30（Asia/Shanghai）｜ 检索窗口 PubMed edat 2026/10/31 – 2026/11/03 ｜ 情报发生日期 2026-11-03
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摘要（PubMed 原文节选）
In clustered competing risks data, both event times within the same cluster and competing events are often correlated. This dependency can be modeled using cluster-specific frailties. This paper considers both hierarchical likelihood and nonparametric likelihood methods for fitting the so-called shared bivariate frailty model, a special case of the cause-specific competing risks frailty model with two causes of events, to clustered competing risks data. For this purpose, it first theoretically investigates the robustness of the maximum hierarchical likelihood estimators (MHLEs) under the common normal log-frailty assumption, while the true frailty distribution deviates from log-normality. It…
对本项目的价值
参考价值：属「衰弱 / 肌少症功能结局」方向；肌肉骨骼 / 衰弱 / 肌少症栏目可用作本项目文献库与综述素材。 ｜ 价值分 1.5/5（★★☆☆☆）
全文不可及原因
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原文题录
- 中文题名：竞争风险脆弱模型的非参数最大似然估计
- 英文标题：Nonparametric maximum likelihood estimation for competing risks frailty models.
- 期刊与标识：Lifetime Data Anal · PMID 42966835 · DOI 10.1007/s10985-026-09737-7 · EDAT 2026-11-01