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Nonparametric Maximum Likelihood Estimation for Competing Risks Survival Data Subject to Interval Censoring and Truncation

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  • Michael G. Hudgens
  • Glen A. Satten
  • Ira M. Longini

Abstract

Summary. We derive the nonparametric maximum likelihood estimate (NPMLE) of the cumulative incidence functions for competing risks survival data subject to interval censoring and truncation. Since the cumulative incidence function NPMLEs give rise to an estimate of the survival distribution which can be undefined over a potentially larger set of regions than the NPMLE of the survival function obtained ignoring failure type, we consider an alternative pseudolikelihood estimator. The methods are then applied to data from a cohort of injecting drug users in Thailand susceptible to infection from HIV‐1 subtypes B and E.

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  • Michael G. Hudgens & Glen A. Satten & Ira M. Longini, 2001. "Nonparametric Maximum Likelihood Estimation for Competing Risks Survival Data Subject to Interval Censoring and Truncation," Biometrics, The International Biometric Society, vol. 57(1), pages 74-80, March.
    • Handle: RePEc:bla:biomet:v:57:y:2001:i:1:p:74-80
    DOI: 10.1111/j.0006-341X.2001.00074.x
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    Cited by:

    1. Pao-sheng Shen, 2022. "Nonparametric estimation for competing risks survival data subject to left truncation and interval censoring," Computational Statistics, Springer, vol. 37(1), pages 29-42, March.
    2. Michael G. Hudgens & Chenxi Li & Jason P. Fine, 2014. "Parametric likelihood inference for interval censored competing risks data," Biometrics, The International Biometric Society, vol. 70(1), pages 1-9, March.
    3. Lu Mao & Dan-Yu Lin & Donglin Zeng, 2017. "Semiparametric regression analysis of interval-censored competing risks data," Biometrics, The International Biometric Society, vol. 73(3), pages 857-865, September.
    4. Tamalika Koley & Anup Dewanji, 2019. "Revisiting Non-Parametric Maximum Likelihood Estimation of Current Status Data with Competing Risks," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 81(1), pages 39-59, June.
    5. Jiahui Li & Qiqing Yu, 2016. "A consistent NPMLE of the joint distribution function with competing risks data under the dependent masking and right-censoring model," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 22(1), pages 63-99, January.
    6. Michael G. Hudgens & Marloes H. Maathuis & Peter B. Gilbert, 2007. "Nonparametric Estimation of the Joint Distribution of a Survival Time Subject to Interval Censoring and a Continuous Mark Variable," Biometrics, The International Biometric Society, vol. 63(2), pages 372-380, June.
    7. Lu Mao & D. Y. Lin, 2017. "Efficient estimation of semiparametric transformation models for the cumulative incidence of competing risks," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 573-587, March.
    8. Halina Frydman & Michael Szarek, 2009. "Nonparametric Estimation in a Markov “Illness–Death” Process from Interval Censored Observations with Missing Intermediate Transition Status," Biometrics, The International Biometric Society, vol. 65(1), pages 143-151, March.
    9. Somnath Datta & Rajeshwari Sundaram, 2006. "Nonparametric Estimation of Stage Occupation Probabilities in a Multistage Model with Current Status Data," Biometrics, The International Biometric Society, vol. 62(3), pages 829-837, September.
    10. Gürler, Ülkü & Deniz Yenigün, C., 2011. "Full and conditional likelihood approaches for hazard change-point estimation with truncated and censored data," Computational Statistics & Data Analysis, Elsevier, vol. 55(10), pages 2856-2870, October.
    11. Li, Chenxi, 2016. "Cause-specific hazard regression for competing risks data under interval censoring and left truncation," Computational Statistics & Data Analysis, Elsevier, vol. 104(C), pages 197-208.
    12. Li, Chenxi, 2016. "The Fine–Gray model under interval censored competing risks data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 327-344.

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