Shen Pao-Sheng
Department of Statistics, Tunghai University, Taichung, 40704, Taiwan.
Lifetime Data Anal. 2025 Jan;31(1):76-101. doi: 10.1007/s10985-024-09641-y. Epub 2024 Nov 17.
Interval sampling is widely used for collection of disease registry data, which typically report incident cases during a certain time period. Such sampling scheme induces doubly truncated data if the failure time can be observed exactly and doubly truncated and interval censored (DTIC) data if the failure time is known only to lie within an interval. In this article, we consider nonparametric estimation of the cumulative incidence functions (CIF) using doubly-truncated and interval-censored competing risks (DTIC-C) data obtained from interval sampling scheme. Using the approach of Shen (Stat Methods Med Res 31:1157-1170, 2022b), we first obtain the nonparametric maximum likelihood estimator (NPMLE) of the distribution function of failure time ignoring failure types. Using the NPMLE, we proposed nonparametric estimators of the CIF with DTIC-C data and establish consistency of the proposed estimators. Simulation studies show that the proposed estimator performs well for finite sample size.
间隔抽样广泛用于疾病登记数据的收集，这类数据通常报告特定时间段内的发病病例。如果能够精确观察到失效时间，这种抽样方案会产生双重截断数据；如果失效时间仅已知位于某个区间内，则会产生双重截断和区间删失（DTIC）数据。在本文中，我们考虑使用从间隔抽样方案获得的双重截断和区间删失竞争风险（DTIC-C）数据对累积发病率函数（CIF）进行非参数估计。采用Shen（《统计方法与医学研究》31:1157 - 1170，2022b）的方法，我们首先获得忽略失效类型的失效时间分布函数的非参数极大似然估计（NPMLE）。利用该NPMLE，我们提出了具有DTIC-C数据的CIF的非参数估计量，并建立了所提估计量的一致性。模拟研究表明，所提估计量在有限样本量情况下表现良好。
