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Semiparametric Regression Analysis of Clustered Interval-Censored Failure Time Data with Informative Cluster Size

Author

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  • Zhang Xinyan

    (School of Public Health, Harvard University, 651 Huntington Ave. FXB 502, Boston, MA 02115, USA)

  • Sun Jianguo

    (Department of Statistics, University of Missouri, Columbia, MO 65211, USA)

Abstract

Clustered interval-censored failure time data are commonly encountered in many medical settings. In such situations, one issue that often arises in practice is that the cluster size is related to the risk for the outcome of interest. It is well-known that ignoring the informativeness of the cluster size can result in biased parameter estimates. In this article, we consider regression analysis of clustered interval-censored data with informative cluster size with the focus on semiparametric methods. For the problem, two approaches are presented and investigated. One is a within-cluster resampling procedure and the other is a weighted estimating equation approach. Unlike previously published methods, the new approaches take into account cluster sizes and heterogeneous correlation structures without imposing strong parametric assumptions. A simulation experiment is carried out to evaluate the performance of the proposed approaches and indicates that they perform well for practical situations. The approaches are applied to a lymphatic filariasis study that motivated this study.

Suggested Citation

  • Zhang Xinyan & Sun Jianguo, 2013. "Semiparametric Regression Analysis of Clustered Interval-Censored Failure Time Data with Informative Cluster Size," The International Journal of Biostatistics, De Gruyter, vol. 9(2), pages 205-214, August.
    • Handle: RePEc:bpj:ijbist:v:9:y:2013:i:2:p:205-214:n:6
    DOI: 10.1515/ijb-2012-0047
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    References listed on IDEAS

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    1. Xiuyu J. Cong & Guosheng Yin & Yu Shen, 2007. "Marginal Analysis of Correlated Failure Time Data with Informative Cluster Sizes," Biometrics, The International Biometric Society, vol. 63(3), pages 663-672, September.
    2. John M. Williamson & Somnath Datta & Glen A. Satten, 2003. "Marginal Analyses of Clustered Data When Cluster Size Is Informative," Biometrics, The International Biometric Society, vol. 59(1), pages 36-42, March.
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