  In biometrics and related fields, the Cox proportional hazards model are
widely used to analyze with covariate adjustment. However, when some covariates
are not observed, an unbiased estimator usually cannot be obtained. Even if
there are some unmeasured covariates, instrumental variable methods can be
applied under some assumptions. In this paper, we propose the new instrumental
variable estimator for the Cox proportional hazards model. The estimator is the
similar feature as Martinez-Camblor et al., 2019, but not the same exactly; we
use an idea of limited-information maximum likelihood. We show that the
estimator has good theoretical properties. Also, we confirm properties of our
method and previous methods through simulations datasets.
