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Interval‐censored data with repeated measurements and a cured subgroup

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  • Jialiang Li
  • Shuangge Ma

Abstract

Summary. The hypobaric decompression sickness data study was conducted by the National Aeronautics and Space Administration to investigate the risk of decompression sickness in hypobaric environments. The quantity of interest is the time to onset of grade IV venous gas emboli, which was mixed case interval censored because of measurement limitations. In the study, some subjects participated in multiple experiments, leading to repeated and correlated measurements on those subjects. In addition, it has been suggested that some subjects had a much lower risk of developing grade IV venous gas emboli than others, i.e. those subjects were immune from the event of interest (or ‘cured’). We propose to use two‐part models, where the first part describes the probability of cure and the second part describes the survival for susceptible subjects. We use two random effects to account for the correlated nature of measurements. A leverage bootstrap approach is proposed for model diagnosis. A simulation study shows satisfactory performance of the estimation and diagnosis approaches proposed. Model estimation and evaluation of the hypobaric decompression sickness data are carefully investigated.

Suggested Citation

  • Jialiang Li & Shuangge Ma, 2010. "Interval‐censored data with repeated measurements and a cured subgroup," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 59(4), pages 693-705, August.
    • Handle: RePEc:bla:jorssc:v:59:y:2010:i:4:p:693-705
    DOI: 10.1111/j.1467-9876.2009.00702.x
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    References listed on IDEAS

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    Cited by:

    1. Xiaochao Xia & Binyan Jiang & Jialiang Li & Wenyang Zhang, 2016. "Low-dimensional confounder adjustment and high-dimensional penalized estimation for survival analysis," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(4), pages 547-569, October.
    2. Lu Wang & Pang Du & Hua Liang, 2012. "Two-Component Mixture Cure Rate Model with Spline Estimated Nonparametric Components," Biometrics, The International Biometric Society, vol. 68(3), pages 726-735, September.
    3. Hu, Tao & Xiang, Liming, 2016. "Partially linear transformation cure models for interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 257-269.
    4. Varadan Sevilimedu & Shuangge Ma & Pamela Hartigan & Tassos C. Kyriakides, 2021. "An Application of the Cure Model to a Cardiovascular Clinical Trial," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 13(3), pages 402-430, December.

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