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Estimating marginal survival function by adjusting for dependent censoring using many covariates

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Donglin Zeng

Statistics Theory · Submitted 10 Sep 2004

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

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arXiv IDmath/0409180 (version 2)
Pith IDHCKFIPFU, Pith's permanent record of this paper and its review
CitationDonglin Zeng. "Estimating marginal survival function by adjusting for dependent censoring using many covariates." arXiv:math/0409180 (2004). https://pith.science/paper/HCKFIPFU
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What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.31 · human participants 0.30 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.17 · compares with prior results 0.21 · mentions code or data availability 0.02.

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arXiv listing abstract v2. Results and the main claim are underlined; .

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. In this paper, 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.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 One goal in survival analysis of right-censored data is to estimate the marginal survival function in the presence of dependent censoring. background 1.00 0.05 0.03 asserted 0.98
s1 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. background 0.70 0.08 0.03 asserted 0.93
s2 In this paper, 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. method 0.92 0.94 0.16 asserted 1.00
s3 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. result 0.98 0.93 0.77 asserted 1.00

What kind of paper the classifier reports this is: method or tool 0.97 · evidence from proof or derivation 0.99.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

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. In this paper, 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.

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