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Estimating the mean of a mark variable under right censoring on the basis of a state function

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  • Fang, Hong-Bin
  • Wang, Jiantian
  • Deng, Dianliang
  • Tang, Man-Lai

Abstract

A mark variable is a generalization of measurements such as lifetime medical costs and quality-adjusted lifetimes. Recently, analysis of the mark variable has generated significant interest as an important component in health treatment evaluation. In this paper, a novel approach to estimating the mean of the mark variable under right censoring is proposed. The proposed estimator is of a much simpler form than most existing estimators advocated in the literature. Theoretical analysis and simulation studies indicate that the proposed estimator has practical applications due to its simplicity and accuracy. A real data set, from a Multicenter Automatic Defibrillator Implantation Trial (MADIT), is used to illustrate the proposed methodology.

Suggested Citation

  • Fang, Hong-Bin & Wang, Jiantian & Deng, Dianliang & Tang, Man-Lai, 2011. "Estimating the mean of a mark variable under right censoring on the basis of a state function," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1726-1735, April.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:4:p:1726-1735
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    References listed on IDEAS

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    1. Mark J. Laan & Alan Hubbard, 1999. "Locally Efficient Estimation of the Quality-Adjusted Lifetime Distribution with Right-Censored Data and Covariates," Biometrics, The International Biometric Society, vol. 55(2), pages 530-536, June.
    2. Hongwei Zhao & Lili Tian, 2001. "On Estimating Medical Cost and Incremental Cost-Effectiveness Ratios with Censored Data," Biometrics, The International Biometric Society, vol. 57(4), pages 1002-1008, December.
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    Cited by:

    1. Laura M. Yee & Kwun Chuen Gary Chan, 2017. "Nonparametric inference for the joint distribution of recurrent marked variables and recurrent survival time," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 23(2), pages 207-222, April.

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