We study the transformed hazards model with time-dependent covariates observed intermittently for the censored outcome. Existing work assumes the availability of the whole trajectory of the time-dependent covariates, which is unrealistic. 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 performs favorably over existing methods. Applying to a COVID-19 study in Wuhan illustrates the practical utility of our method.

翻译：我们研究了右删失结局中，协变量呈间歇性观测的时间依赖场景下的变换风险模型。现有工作假设时间依赖协变量的完整轨迹可获得，这在实际中并不现实。我们提出将核加权对数似然与筛极大似然估计相结合进行统计推断，该方法稳健且易于实现。我们建立了所提估计量的渐近性质，并为一般核加权筛M估计量构建了严格的理论框架。数值研究验证了理论结果，并表明所提方法优于现有方法。将其应用于武汉的一项COVID-19研究，展示了该方法的实际效用。