Hazard estimation with censoring and measurement error: application to length of pregnancy
被引:0
|
作者:
Fabienne Comte
论文数: 0引用数: 0
h-index: 0
机构:University Paris Descartes,MAP5 UMR CNRS 8145
Fabienne Comte
Adeline Samson
论文数: 0引用数: 0
h-index: 0
机构:University Paris Descartes,MAP5 UMR CNRS 8145
Adeline Samson
Julien J. Stirnemann
论文数: 0引用数: 0
h-index: 0
机构:University Paris Descartes,MAP5 UMR CNRS 8145
Julien J. Stirnemann
机构:
[1] University Paris Descartes,MAP5 UMR CNRS 8145
[2] Sorbonne Paris Cité,Laboratoire Jean Kuntzmann, UMR CNRS 5224
[3] Univ Grenoble-Alpes,Obstetrics and Maternal
[4] University Paris Descartes, Fetal Medicine, GHU Necker
来源:
TEST
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2018年
/
27卷
关键词:
Censored data;
Measurement error;
Survival function estimation;
Hazard rate function estimation;
Nonparametric methods;
Deconvolution;
Length of pregnancy;
62G07;
62N01;
D O I:
暂无
中图分类号:
学科分类号:
摘要:
Estimation of the physiological length of pregnancy is a challenging problem since both the time origin of the pregnancy and the time of onset of labor are partly observed. The time to spontaneous labor is indeed right-censored, and the time of fertilization is only known up to an error. Therefore, data are subject to both censoring and measurement errors. We focus on the case where the measurement errors affect both the variable of interest and the censoring variable, which is the case of the timing of spontaneous delivery among pregnant women. We propose an estimation strategy to estimate the hazard rate of the underlying variable of interest. We explain the model and this strategy and provide L2\documentclass[12pt]{minimal}
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\begin{document}$$L^2$$\end{document}-risk bound for the data driven resulting estimator. We also derive estimators of the survival function and the density. Simulations illustrate the performances of the estimator. Lastly, the method is applied to an original real data set of length of pregnancy to estimate rates of previable births, severe preterm births and prolonged pregnancy and the influence of the cervical length of the first semester.