  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.
