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Likelihood approach for marginal proportional hazards regression in the presence of dependent censoring

Donglin Zeng
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Abstract

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.

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Donglin Zeng. 2005-05-27. Likelihood approach for marginal proportional hazards regression in the presence of dependent censoring. https://doi.org/10.1214/009053604000001291

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