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A partially linear proportional hazards model for current status data

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  • Minggen Lu
  • Christopher S. McMahan

Abstract

For analyzing current status data, a flexible partially linear proportional hazards model is proposed. Modeling flexibility is attained through using monotone splines to approximate the baseline cumulative hazard function, as well as B‐splines to accommodate nonlinear covariate effects. To facilitate model fitting, a computationally efficient and easy to implement expectation‐maximization algorithm is developed through a two‐stage data augmentation process involving carefully structured latent Poisson random variables. Asymptotic normality and the efficiency of the spline estimator of the regression coefficients are established, and the spline estimators of the nonparametric components are shown to possess the optimal rate of convergence under suitable regularity conditions. The finite‐sample performance of the proposed approach is evaluated through Monte Carlo simulation and it is further illustrated using uterine fibroid data arising from a prospective cohort study on early pregnancy.

Suggested Citation

  • Minggen Lu & Christopher S. McMahan, 2018. "A partially linear proportional hazards model for current status data," Biometrics, The International Biometric Society, vol. 74(4), pages 1240-1249, December.
    • Handle: RePEc:bla:biomet:v:74:y:2018:i:4:p:1240-1249
    DOI: 10.1111/biom.12914
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    References listed on IDEAS

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    1. Cai, Bo & Lin, Xiaoyan & Wang, Lianming, 2011. "Bayesian proportional hazards model for current status data with monotone splines," Computational Statistics & Data Analysis, Elsevier, vol. 55(9), pages 2644-2651, September.
    2. Donglin Zeng & Lu Mao & D. Y. Lin, 2016. "Maximum likelihood estimation for semiparametric transformation models with interval-censored data," Biometrika, Biometrika Trust, vol. 103(2), pages 253-271.
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    Cited by:

    1. Tian Tian & Jianguo Sun, 2024. "Variable Selection for Nonlinear Covariate Effects with Interval-Censored Failure Time Data," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 16(1), pages 185-202, April.
    2. Prabhashi W. Withana Gamage & Monica Chaudari & Christopher S. McMahan & Edwin H. Kim & Michael R. Kosorok, 2020. "An extended proportional hazards model for interval-censored data subject to instantaneous failures," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(1), pages 158-182, January.
    3. Prabhashi W. Withana Gamage & Christopher S. McMahan & Lianming Wang, 2023. "A flexible parametric approach for analyzing arbitrarily censored data that are potentially subject to left truncation under the proportional hazards model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 29(1), pages 188-212, January.

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