The nonparametric maximum likelihood estimator in a class of doubly censored current status data models with application to partner studies
一类双重删失现时状态模型的非参数极大似然估计及伴侣研究应用AI 解读 ↓
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一类双重删失现时状态模型的非参数极大似然估计及伴侣研究应用
- 研究问题
- 当感染起点不完全可知、伴侣仅在招募时被观察感染状态时，如何估计其暴露至感染的时间分布，并构造相关泛函的区间？
- 方法与思路
- 作者假定源病例感染时点在已知区间内均匀分布，构建双重删失现时状态模型，以修正的加权相邻违例合并算法计算估计。
- 材料陈述的贡献
- 摘要称，非参数极大似然估计收敛，并提出平滑泛函的置信区间，通过模拟比较和加州伴侣研究展示所提方法。
- 阅读关注点
- 核对已知感染起点分布假设的作用、不同伴侣关系起始时点如何处理，并区分估计量收敛与算法收敛的具体结论。
- 阅读边界
- 仅据版本未注明的索引摘要，无法确定收敛类型、区间有效性条件及实证数值；均匀起点分布假设也未获独立验证。
原文摘要
The California Partners' Study is an ongoing investigation of heterosexual HIV transmission in partners of infected index cases (Padian, et al., 1987; Shiboski & Jewell, 1990). In addition to the HIV-status of the partner at the recruiting time one also observes the initial time of the partnership and a lower bound for the infection time of the index case. Following Jewell, Malani & Vittinghoff (1994) we assume that the infection time of the index case is uniformly distributed over the interval determined by the lower bound and the recruiting time, but no further assumptions are made. We consider an NPMLE of the distribution of the time T the partner is exposed to an infected index partner until infection. We show that the model is a doubly censored current status data model as introduced in Jewell, Malani & Vittinghoff (1994) with a special known distribution of the origin. We provide a modified iterative Weighted Pool Adjacent Violator Algorithm for computation of the NPMLE. It is shown that the NPMLE converges. In addition, we propose confidence intervals for smooth functionals of the distribution of T. Simulations show good performance of the algorithm, confidence intervals and provide a practical comparison of this NPMLE with the NPMLE if all partnerships are already in existence at the infection time of the index case as used in Shiboski & Jewell (1990). We apply our methodology to the California Partners' Study. We discuss the implications of our results for doubly censored current status data models with other known distributions of the origin.