We consider semiparametric analysis of competing risks data subject to mixed case interval censoring. The Fine-Gray model (Fine & Gray, 1999) is used to model the cumulative incidence function and is coupled with sieve semiparametric maximum likelihood estimation based on univariate or multivariate likelihood. The univariate likelihood of cause-specific data enables separate estimation of cumulative incidence function for each competing risk, in contrast with the multivariate likelihood of full data which estimates cumulative incidence functions for multiple competing risks jointly. Under both likelihoods and certain regularity conditions, we show that the regression parameter estimator is asymptotically normal and semiparametrically efficient, although the spline-based sieve estimator of the baseline cumulative subdistribution hazard converges at a rate slower than root-n. The proposed method is evaluated by simulation studies regarding its finite sample performance and is illustrated by a competing risk analysis of data from an dementia cohort study.

中文翻译：

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区间删失竞争风险数据下的 Fine-Gray 模型

我们考虑对受混合案例区间审查的竞争风险数据进行半参数分析。Fine-Gray 模型 (Fine & Gray, 1999) 用于对累积关联函数建模，并与基于单变量或多变量似然的筛分半参数最大似然估计相结合。与联合估计多个竞争风险的累积发生率函数的完整数据的多变量似然相比，特定原因数据的单变量似然能够单独估计每个竞争风险的累积发生率函数。在似然和一定规律性条件下，我们证明回归参数估计量是渐近正态和半参数有效的，尽管基线累积子分布风险的基于样条的筛估计器以比 root-n 慢的速率收敛。所提出的方法通过关于其有限样本性能的模拟研究进行评估，并通过对痴呆症队列研究数据的竞争风险分析进行说明。