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A semi-parametric weighted likelihood approach for regression analysis of bivariate interval-censored outcomes from case-cohort studies

Author

Listed:
  • Yichen Lou

    (Jilin University)

  • Peijie Wang

    (Jilin University)

  • Jianguo Sun

    (University of Missouri)

Abstract

The case-cohort design was developed to reduce costs when disease incidence is low and covariates are difficult to obtain. However, most of the existing methods are for right-censored data and there exists only limited research on interval-censored data, especially on regression analysis of bivariate interval-censored data. Interval-censored failure time data frequently occur in many areas and a large literature on their analyses has been established. In this paper, we discuss the situation of bivariate interval-censored data arising from case-cohort studies. For the problem, a class of semiparametric transformation frailty models is presented and for inference, a sieve weighted likelihood approach is developed. The large sample properties, including the consistency of the proposed estimators and the asymptotic normality of the regression parameter estimators, are established. Moreover, a simulation is conducted to assess the finite sample performance of the proposed method and suggests that it performs well in practice.

Suggested Citation

  • Yichen Lou & Peijie Wang & Jianguo Sun, 2023. "A semi-parametric weighted likelihood approach for regression analysis of bivariate interval-censored outcomes from case-cohort studies," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(3), pages 628-653, July.
    • Handle: RePEc:spr:lifeda:v:29:y:2023:i:3:d:10.1007_s10985-023-09593-9
    DOI: 10.1007/s10985-023-09593-9
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    References listed on IDEAS

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