  In many public health problems, an important goal is to identify the effect
of some treatment/intervention on the risk of failure for the whole population.
A marginal proportional hazards regression model is often used to analyze such
an effect. When dependent censoring is explained by many auxiliary covariates,
we utilize two working models to condense high-dimensional covariates to
achieve dimension reduction. Then the estimator of the treatment effect is
obtained by maximizing a pseudo-likelihood function over a sieve space. Such an
estimator is shown to be consistent and asymptotically normal when either of
the two working models is correct; additionally, when both working models are
correct, its asymptotic variance is the same as the semiparametric efficiency
bound.
