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Estimation of the Association for Bivariate Interval‐censored Failure Time Data

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  • LIUQUAN SUN
  • LIANMING WANG
  • JIANGUO SUN

Abstract

. Multivariate failure time data frequently occur in medical studies and the dependence or association among survival variables is often of interest (Biometrics, 51, 1995, 1384; Stat. Med., 18, 1999, 3101; Biometrika, 87, 2000, 879; J. Roy. Statist. Soc. Ser. B, 65, 2003, 257). We study the problem of estimating the association between two related survival variables when they follow a copula model and only bivariate interval‐censored failure time data are available. For the problem, a two‐stage estimation procedure is proposed and the asymptotic properties of the proposed estimator are established. Simulation studies are conducted to assess the finite sample properties of the presented estimate and the results suggest that the method works well for practical situations. An example from an acquired immunodeficiency syndrome clinical trial is discussed.

Suggested Citation

  • Liuquan Sun & Lianming Wang & Jianguo Sun, 2006. "Estimation of the Association for Bivariate Interval‐censored Failure Time Data," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 33(4), pages 637-649, December.
    • Handle: RePEc:bla:scjsta:v:33:y:2006:i:4:p:637-649
    DOI: 10.1111/j.1467-9469.2006.00502.x
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    Cited by:

    1. Tao Sun & Yu Cheng & Ying Ding, 2023. "An information ratio‐based goodness‐of‐fit test for copula models on censored data," Biometrics, The International Biometric Society, vol. 79(3), pages 1713-1725, September.
    2. Bernard Rosner & Camden Bay & Robert J. Glynn & Gui-shuang Ying & Maureen G. Maguire & Mei-Ling Ting Lee, 2023. "Estimation and testing for clustered interval-censored bivariate survival data with application using the semi-parametric version of the Clayton–Oakes model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(4), pages 854-887, October.
    3. Hao Liu & Jing Qin, 2018. "Semiparametric probit models with univariate and bivariate current†status data," Biometrics, The International Biometric Society, vol. 74(1), pages 68-76, March.
    4. Shu Jiang & Richard J. Cook, 2020. "A Mixture Model for Bivariate Interval-Censored Failure Times with Dependent Susceptibility," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 12(1), pages 37-62, April.
    5. Kouros Owzar & Sin-Ho Jung & Pranab Kumar Sen, 2007. "A Copula Approach for Detecting Prognostic Genes Associated With Survival Outcome in Microarray Studies," Biometrics, The International Biometric Society, vol. 63(4), pages 1089-1098, December.

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