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Estimating marginal survival function by adjusting for dependent censoring using many covariates. (English) Zbl 1047.62092

Summary: One goal in survival analysis of right-censored data is to estimate the marginal survival function in the presence of dependent censoring. When many auxiliary covariates are sufficient to explain the dependent censoring, estimation based on either a semiparametric model or a nonparametric model of the conditional survival function can be problematic due to the high dimensionality of the auxiliary information.
We use two working models to condense these high-dimensional covariates in dimension reduction; then an estimate of the marginal survival function can be derived nonparametrically in a low-dimensional space. We show that such an estimator has the following double robust property: when either working model is correct, the estimator is consistent and asymptotically Gaussian; when both working models are correct, the asymptotic variance attains the efficiency bound.

MSC:

62N02 Estimation in survival analysis and censored data
62N01 Censored data models
62G07 Density estimation
62G20 Asymptotic properties of nonparametric inference
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References:

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