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Regression analysis of clustered interval-censored failure time data with the additive hazards model

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  • Junlong Li
  • Chunjie Wang
  • Jianguo Sun

Abstract

This paper discusses regression analysis of clustered failure time data, which means that the failure times of interest are clustered into small groups instead of being independent. Clustering occurs in many fields such as medical studies. For the problem, a number of methods have been proposed, but most of them apply only to clustered right-censored data. In reality, the failure time data is often interval-censored. That is, the failure times of interest are known only to lie in certain intervals. We propose an estimating equation-based approach for regression analysis of clustered interval-censored failure time data generated from the additive hazards model. A major advantage of the proposed method is that it does not involve the estimation of any baseline hazard function. Both asymptotic and finite sample properties of the proposed estimates of regression parameters are established and the method is illustrated by the data arising from a lymphatic filariasis study.

Suggested Citation

  • Junlong Li & Chunjie Wang & Jianguo Sun, 2012. "Regression analysis of clustered interval-censored failure time data with the additive hazards model," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 24(4), pages 1041-1050, December.
    • Handle: RePEc:taf:gnstxx:v:24:y:2012:i:4:p:1041-1050
    DOI: 10.1080/10485252.2012.720256
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    References listed on IDEAS

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    1. D. Ghosh, 2001. "Efficiency Considerations in the Additive Hazards Model with Current Status Data," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(3), pages 367-376, November.
    2. Torben Martinussen, 2002. "Efficient estimation in additive hazards regression with current status data," Biometrika, Biometrika Trust, vol. 89(3), pages 649-658, August.
    3. Eric A. Ross & Dirk Moore, 1999. "Modeling Clustered, Discrete, or Grouped Time Survival Data with Covariates," Biometrics, The International Biometric Society, vol. 55(3), pages 813-819, September.
    4. Jianwen Cai & Donglin Zeng, 2011. "Additive Mixed Effect Model for Clustered Failure Time Data," Biometrics, The International Biometric Society, vol. 67(4), pages 1340-1351, December.
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    Cited by:

    1. Chen, Ling & Sun, Jianguo & Xiong, Chengjie, 2016. "A multiple imputation approach to the analysis of clustered interval-censored failure time data with the additive hazards model," Computational Statistics & Data Analysis, Elsevier, vol. 103(C), pages 242-249.

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