Semiparametric efficient estimation for shared-frailty models with doubly-censored clustered data
双侧删失聚类数据共享脆弱性模型的半参数有效估计AI 解读 ↓
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双侧删失聚类数据共享脆弱性模型的半参数有效估计
- 研究问题
- 聚类生存数据同时存在左删失与右删失时，如何估计共享脆弱性模型，并处理计算困难和参数推断问题？
- 方法与思路
- 作者采用似然方法求非参数极大似然估计，提出处理删失与脆弱性变量的新算法，并开展模拟和乙肝家庭数据应用。
- 材料陈述的贡献
- 摘要称建立估计量的渐近性质和有限维参数的半参数有效性，讨论 bootstrap 标准误一致性，并报告算法改进。
- 阅读关注点
- 应核对脆弱性及删失机制的设定、模型识别、算法收敛条件、有效性证明，以及模拟比较与标准误计算方式。
- 阅读边界
- 仅据版本未注明的索引摘要；有效性的条件、算法改进幅度和应用数值未提供，不能确认其在其他数据中的表现。
原文摘要
In this paper, we investigate frailty models for clustered survival data that are subject to both left- and right-censoring, termed "doubly-censored data". This model extends current survival literature by broadening the application of frailty models from right-censoring to a more complicated situation with additional left censoring. Our approach is motivated by a recent Hepatitis B study where the sample consists of families. We adopt a likelihood approach that aims at the nonparametric maximum likelihood estimators (NPMLE). A new algorithm is proposed, which not only works well for clustered data but also improve over existing algorithm for independent and doubly-censored data, a special case when the frailty variable is a constant equal to one. This special case is well known to be a computational challenge due to the left censoring feature of the data. The new algorithm not only resolves this challenge but also accommodate the additional frailty variable effectively. Asymptotic properties of the NPMLE are established along with semi-parametric efficiency of the NPMLE for the finite-dimensional parameters. The consistency of Bootstrap estimators for the standard errors of the NPMLE is also discussed. We conducted some simulations to illustrate the numerical performance and robustness of the proposed algorithm, which is also applied to the Hepatitis B data.