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Analyzing right-censored and length-biased data with varying-coefficient transformation model

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  • Lin, Cunjie
  • Zhou, Yong

Abstract

Right-censored and length-biased data arise in many applications, including disease screening and epidemiological cohort studies. It is challenging to analyze such data, since independent censoring assumption is violated in the presence of biased sampling. In this paper, we study the varying-coefficient transformation models with right-censored and length-biased data. We use the local linear fitting technique and propose estimators of varying coefficients by constructing the local inverse probability weighted estimating equations. We have shown that the proposed estimators are consistent and asymptotically normal and their variances can be estimated consistently. We pay special attention to the case where the censoring variable depends on the covariates. We conduct simulation studies to assess the performance of the proposed method and demonstrate its application on a real data example.

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  • Lin, Cunjie & Zhou, Yong, 2014. "Analyzing right-censored and length-biased data with varying-coefficient transformation model," Journal of Multivariate Analysis, Elsevier, vol. 130(C), pages 45-63.
    • Handle: RePEc:eee:jmvana:v:130:y:2014:i:c:p:45-63
    DOI: 10.1016/j.jmva.2014.05.003
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    Cited by:

    1. Lin, Cunjie & Zhou, Yong, 2016. "Semiparametric varying-coefficient model with right-censored and length-biased data," Journal of Multivariate Analysis, Elsevier, vol. 152(C), pages 119-144.
    2. Yu Shen & Jing Ning & Jing Qin, 2017. "Nonparametric and semiparametric regression estimation for length-biased survival data," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(1), pages 3-24, January.
    3. Chengbo Li & Yong Zhou, 2021. "The estimation for the general additive–multiplicative hazard model using the length-biased survival data," Statistical Papers, Springer, vol. 62(1), pages 53-74, February.

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