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Interval-censored data with repeated measurements and a cured subgroup

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  • Jialiang Li
  • Shuangge Ma

Abstract

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. Copyright (c) 2010 Royal Statistical Society.

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.
  • Handle: RePEc:bla:jorssc:v:59:y:2010:i:4:p:693-705
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    References listed on IDEAS

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    1. Richard J. Cook & Leilei Zeng & Ker-Ai Lee, 2008. "A Multistate Model for Bivariate Interval-Censored Failure Time Data," Biometrics, The International Biometric Society, vol. 64(4), pages 1100-1109, December.
    2. Rinku Sutradhar & Richard J. Cook, 2008. "Analysis of interval-censored data from clustered multistate processes: application to joint damage in psoriatic arthritis," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 57(5), pages 553-566.
    3. R. Bock & Murray Aitkin, 1981. "Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm," Psychometrika, Springer;The Psychometric Society, vol. 46(4), pages 443-459, December.
    4. Jian-Jian Ren, 2003. "Goodness of fit tests with interval censored data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 30(1), pages 211-226.
    5. Zeng, Leilei & Cook, Richard J., 2007. "Transition Models for Multivariate Longitudinal Binary Data," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 211-223, March.
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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. 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.
    3. 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.

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