Quantile Regression for Right-Censored and Length-Biased Data

Xue-rong CHEN, Yong ZHOU

Acta Mathematicae Applicatae Sinica(English Series) ›› 2012, Vol. 28 ›› Issue (3) : 443-462.

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Acta Mathematicae Applicatae Sinica(English Series) ›› 2012, Vol. 28 ›› Issue (3) : 443-462. DOI: 10.1007/s10255-012-0157-3
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Quantile Regression for Right-Censored and Length-Biased Data

  • Xue-rong CHEN, Yong ZHOU
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Abstract

Length-biased data arise in many important fields, including epidemiological cohort studies, cancer screening trials and labor economics. Analysis of such data has attracted much attention in the literature. In this paper we propose a quantile regression approach for analyzing right-censored and length-biased data. We derive an inverse probability weighted estimating equation corresponding to the quantile regression to correct the bias due to length-bias sampling and informative censoring. This method can easily handle informative censoring induced by length-biased sampling. This is an appealing feature of our proposed method since it is generally difficult to obtain unbiased estimates of risk factors in the presence of length-bias and informative censoring. We establish the consistency and asymptotic distribution of the proposed estimator using empirical process techniques. A resampling method is adopted to estimate the variance of the estimator. We conduct simulation studies to evaluate its finite sample performance and use a real data set to illustrate the application of the proposed method.

Key words

length-biased sampling / right-censored / information censoring / quantile regression / estimating equations / resampling method

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Xue-rong CHEN, Yong ZHOU. Quantile Regression for Right-Censored and Length-Biased Data. Acta Mathematicae Applicatae Sinica(English Series), 2012, 28(3): 443-462 https://doi.org/10.1007/s10255-012-0157-3

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Funding

Zhou’s work was supported by the following funding bodies: National Natural Science Funds for Distinguished Young Scholar (No. 70825004), Creative Research Groups of China (No. 10721101), Shanghai University of Finance and Economics Project 211 Phase III and Shanghai Leading Academic Discipline Project (No. B803).
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