Disease progression is often monitored by intermittent follow-up “visits” in longitudinal cohort studies, resulting in interval-censored failure time outcomes. Furthermore, the timing and frequency of visits is often found related to a person's history of disease-related variables in practice. This article develops a semiparametric estimation approach using weighted binomial regression and a kernel smoother to analyze interval-censored failure time data. Visit times are allowed to be subject-specific and outcome-dependent. We consider a collection of widely used semiparametric regression models, including additive hazards and linear transformation models. For additive hazards models, the nonparametric component has a closed-form estimator and the estimators of regression coefficients are shown to be asymptotically multivariate normal with sandwich-type covariance matrices. Simulations are conducted to examine the finite sample performance of the proposed estimators. A data set from the Toronto Psoriatic Arthritis (PsA) Cohort Study is used to illustrate the proposed methodology.

中文翻译：

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使用与结果相关的观察方案对间隔删失故障时间数据进行半参数分析

疾病进展通常通过纵向队列研究中的间歇性随访“访问”来监测，从而导致间隔审查的失败时间结果。此外，在实践中经常发现访问的时间和频率与一个人的疾病相关变量的历史有关。本文开发了一种半参数估计方法，使用加权二项式回归和核平滑器来分析间隔删失的故障时间数据。允许访问时间是特定于主题和结果的。我们考虑了一系列广泛使用的半参数回归模型，包括加性风险和线性变换模型。对于加性危害模型，非参数分量具有封闭形式的估计量，回归系数的估计量显示为具有三明治型协方差矩阵的渐近多元正态。进行模拟以检查所提出的估计器的有限样本性能。来自多伦多银屑病关节炎 (PsA) 队列研究的数据集用于说明所提出的方法。