dorsal/arxiv
View SchemaSemiparametric estimation of GLMs with interval-censored covariates via an augmented Turnbull estimator
| Authors | Andrea Toloba, Klaus Langohr, Guadalupe Gómez Melis |
|---|---|
| Categories | |
| ArXiv ID | 2601.08996vv1 |
| URL | https://arxiv.org/abs/2601.08996 |
| License | http://arxiv.org/licenses/nonexclusive-distrib/1.0/ |
Abstract
Interval-censored covariates are frequently encountered in biomedical studies, particularly in time-to-event data or when measurements are subject to detection or quantification limits. Yet, the estimation of regression models with interval-censored covariates remains methodologically underdeveloped. In this article, we address the estimation of generalized linear models when one covariate is subject to interval censoring. We propose a likelihood-based approach, GELc, that builds upon an augmented version of Turnbull's nonparametric estimator for interval-censored data. We prove that the GELc estimator is consistent and asymptotically normal under mild regularity conditions, with available standard errors. Simulation studies demonstrate favorable finite-sample performance of the estimator and satisfactory coverage of the confidence intervals. Finally, we illustrate the method using two real-world applications: the AIDS Clinical Trials Group Study 359 and an observational nutrition study on circulating carotenoids. The proposed methodology is available as an R package at github.com/atoloba/ICenCov.
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"abstract": "Interval-censored covariates are frequently encountered in biomedical studies, particularly in time-to-event data or when measurements are subject to detection or quantification limits. Yet, the estimation of regression models with interval-censored covariates remains methodologically underdeveloped. In this article, we address the estimation of generalized linear models when one covariate is subject to interval censoring. We propose a likelihood-based approach, GELc, that builds upon an augmented version of Turnbull\u0027s nonparametric estimator for interval-censored data. We prove that the GELc estimator is consistent and asymptotically normal under mild regularity conditions, with available standard errors. Simulation studies demonstrate favorable finite-sample performance of the estimator and satisfactory coverage of the confidence intervals. Finally, we illustrate the method using two real-world applications: the AIDS Clinical Trials Group Study 359 and an observational nutrition study on circulating carotenoids. The proposed methodology is available as an R package at github.com/atoloba/ICenCov.",
"arxiv_id": "2601.08996",
"authors": [
"Andrea Toloba",
"Klaus Langohr",
"Guadalupe G\u00f3mez Melis"
],
"categories": [
"stat.ME",
"q-bio.QM"
],
"license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
"title": "Semiparametric estimation of GLMs with interval-censored covariates via an augmented Turnbull estimator",
"url": "https://arxiv.org/abs/2601.08996",
"version": "v1"
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