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Regression analysis of bivariate current status data under the Gamma-frailty proportional hazards model using the EM algorithm

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  • Wang, Naichen
  • Wang, Lianming
  • McMahan, Christopher S.

Abstract

The Gamma-frailty proportional hazards (PH) model is commonly used to analyze correlated survival data. Despite this model’s popularity, the analysis of correlated current status data under the Gamma-frailty PH model can prove to be challenging using traditional techniques. Consequently, in this paper we develop a novel expectation–maximization (EM) algorithm under the Gamma-frailty PH model to study bivariate current status data. Our method uses a monotone spline representation to approximate the unknown conditional cumulative baseline hazard functions. Proceeding in this fashion leads to the estimation of a finite number of parameters while simultaneously allowing for modeling flexibility. The derivation of the proposed EM algorithm relies on a three-stage data augmentation involving Poisson latent variables. The resulting algorithm is easy to implement, robust to initialization, and enjoys quick convergence. Simulation results suggest that the proposed method works well and is robust to the misspecification of the frailty distribution. Our methodology is used to analyze chlamydia and gonorrhea data collected by the Nebraska Public Health Laboratory as a part of the Infertility Prevention Project.

Suggested Citation

  • Wang, Naichen & Wang, Lianming & McMahan, Christopher S., 2015. "Regression analysis of bivariate current status data under the Gamma-frailty proportional hazards model using the EM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 140-150.
    • Handle: RePEc:eee:csdana:v:83:y:2015:i:c:p:140-150
    DOI: 10.1016/j.csda.2014.10.013
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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. David B. Dunson & Gregg E. Dinse, 2002. "Bayesian Models for Multivariate Current Status Data with Informative Censoring," Biometrics, The International Biometric Society, vol. 58(1), pages 79-88, March.
    3. Wang, Lianming & Lin, Xiaoyan, 2011. "A Bayesian approach for analyzing case 2 interval-censored data under the semiparametric proportional odds model," Statistics & Probability Letters, Elsevier, vol. 81(7), pages 876-883, July.
    4. Wen, Chi-Chung & Chen, Yi-Hau, 2011. "Nonparametric maximum likelihood analysis of clustered current status data with the gamma-frailty Cox model," Computational Statistics & Data Analysis, Elsevier, vol. 55(2), pages 1053-1060, February.
    5. William B. Goggins & Dianne M. Finkelstein, 2000. "A Proportional Hazards Model for Multivariate Interval-Censored Failure Time Data," Biometrics, The International Biometric Society, vol. 56(3), pages 940-943, September.
    6. Guosheng Yin & Joseph G. Ibrahim, 2005. "A Class of Bayesian Shared Gamma Frailty Models with Multivariate Failure Time Data," Biometrics, The International Biometric Society, vol. 61(1), pages 208-216, March.
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    Cited by:

    1. González, M. & Minuesa, C. & del Puerto, I., 2016. "Maximum likelihood estimation and expectation–maximization algorithm for controlled branching processes," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 209-227.
    2. Xu, Yang & Zhao, Shishun & Hu, Tao & Sun, Jianguo, 2021. "Variable selection for generalized odds rate mixture cure models with interval-censored failure time data," Computational Statistics & Data Analysis, Elsevier, vol. 156(C).
    3. Qingning Zhou & Tao Hu & Jianguo Sun, 2017. "A Sieve Semiparametric Maximum Likelihood Approach for Regression Analysis of Bivariate Interval-Censored Failure Time Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 664-672, April.
    4. 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.
    5. Petti, Danilo & Eletti, Alessia & Marra, Giampiero & Radice, Rosalba, 2022. "Copula link-based additive models for bivariate time-to-event outcomes with general censoring scheme," Computational Statistics & Data Analysis, Elsevier, vol. 175(C).
    6. Li, Shuwei & Hu, Tao & Wang, Peijie & Sun, Jianguo, 2017. "Regression analysis of current status data in the presence of dependent censoring with applications to tumorigenicity experiments," Computational Statistics & Data Analysis, Elsevier, vol. 110(C), pages 75-86.
    7. 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.
    8. Donglin Zeng & Fei Gao & D. Y. Lin, 2017. "Maximum likelihood estimation for semiparametric regression models with multivariate interval-censored data," Biometrika, Biometrika Trust, vol. 104(3), pages 505-525.
    9. Gamage, Prabhashi W. Withana & McMahan, Christopher S. & Wang, Lianming & Tu, Wanzhu, 2018. "A Gamma-frailty proportional hazards model for bivariate interval-censored data," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 354-366.

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