We study the transformed hazards model with intermittently observed time-dependent covariates for the censored outcome. Existing work assumes the availability of the whole trajectory of the time-dependent covariates, which is not realistic. We propose to combine kernel-weighted log-likelihood and sieve maximum log-likelihood estimation to conduct statistical inference. The method is robust and easy to implement. We establish the asymptotic properties of the proposed estimator and contribute to a rigorous theoretical framework for general kernel-weighted sieve M-estimators. Numerical studies corroborate our theoretical results and show that the proposed method has favorable performance over existing methods. An application to a COVID-19 study in Wuhan illustrates the practical utility of our method.

翻译：我们研究了在删失结局中，观测时间不连续的时间依赖性协变量下的变换风险模型。现有研究假设时间依赖性协变量的整个轨迹是可用的，但这在实际中并不现实。我们提出结合核加权对数似然和筛最大对数似然估计进行统计推断。该方法稳健且易于实现。我们建立了所提出估计量的渐近性质，并为广义核加权筛M估计量构建了严格的理论框架。数值研究证实了我们的理论结果，并表明所提出方法在性能上优于现有方法。在武汉一项COVID-19研究中的应用展示了我们方法的实际效用。