Nonparametric inference on bivariate survival data with interval sampling: association estimation and testing
区间抽样下二元生存数据的非参数关联估计与检验AI 解读 ↓
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区间抽样下二元生存数据的非参数关联估计与检验
- 研究问题
- 当数据按特定日历区间内发生的疾病事件被纳入时，如何估计两个失效时间的关联，并检验分析所依赖的准独立性？
- 方法与思路
- 作者基于 Kendall's tau，按选择概率倒数加权可比较且可排序的观测对估计关联，并用二元条件 tau 构造准独立性检验。
- 材料陈述的贡献
- 摘要称，论文提供区间抽样下的非参数关联估计与准独立性检验；模拟显示中等样本量下表现良好，并以两个真实数据集说明。
- 阅读关注点
- 应核对可观察区域内准独立性的定义、观测对的可比条件、选择概率的计算或估计，以及抽样与随访机制对推断的限制。
- 阅读边界
- 仅据 OpenAlex 索引摘要，稿件版本未注明；区间抽样不能混同区间删失，选择概率条件和模拟数值未提供。
原文摘要
In many biomedical applications, interest focuses on the occurrence of two or more consecutive failure events and the relationship between event times, such as age of disease onset and residual lifetime. Bivariate survival data with interval sampling arise frequently when disease registries or surveillance systems collect data based on disease incidence occurring within a specific calendar time interval. The initial event is then retrospectively confirmed and the subsequent failure event may be observed during follow-up. In life history studies, the initial and two consecutive failure events could correspond to birth, disease onset and death. The statistical features and bias of observed data in relation to interval sampling were discussed by Zhu & Wang (2012). Here we propose nonparametric estimation of the association between bivariate failure times based on Kendall's tau for data collected with interval sampling. A nonparametric estimator is given, where the contribution of each comparable and orderable pair is weighted by the inverse of the associated selection probability. Analysis methods for bivariate survival data with interval sampling rely on the assumption of quasi-independence, i.e., that bivariate failure times and the time of the initial event are independent in the observable region. This paper develops a nonparametric test of quasi-independence based on a bivariate conditional Kendall's tau for such data. Simulation studies demonstrate that the association estimator and testing procedure perform well with moderate sample sizes. Illustrations with two real datasets are provided.