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Inference based on the EM algorithm for the competing risks model with masked causes of failure

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  • Radu V. Craiu

Abstract

In this paper we propose inference methods based on the EM algorithm for estimating the parameters of a weakly parameterised competing risks model with masked causes of failure and second-stage data. With a carefully chosen definition of complete data, the maximum likelihood estimation of the cause-specific hazard functions and of the masking probabilities is performed via an EM algorithm. Both the E- and M-steps can be solved in closed form under the full model and under some restricted models of interest. We illustrate the flexibility of the method by showing how grouped data and tests of common hypotheses in the literature on missing cause of death can be handled. The method is applied to a real dataset and the asymptotic and robustness properties of the estimators are investigated through simulation. Copyright Biometrika Trust 2004, Oxford University Press.

Suggested Citation

  • Radu V. Craiu, 2004. "Inference based on the EM algorithm for the competing risks model with masked causes of failure," Biometrika, Biometrika Trust, vol. 91(3), pages 543-558, September.
    • Handle: RePEc:oup:biomet:v:91:y:2004:i:3:p:543-558
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    Cited by:

    1. Giorgos Bakoyannis & Ying Zhang & Constantin T. Yiannoutsos, 2020. "Semiparametric regression and risk prediction with competing risks data under missing cause of failure," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 26(4), pages 659-684, October.
    2. Suzanne E. Dahlberg & Molin Wang, 2007. "A Proportional Hazards Cure Model for the Analysis of Time to Event with Frequently Unidentifiable Causes," Biometrics, The International Biometric Society, vol. 63(4), pages 1237-1244, December.
    3. Qiqing Yu & G. Wong & Hao Qin & Jiaping Wang, 2012. "Random partition masking model for censored and masked competing risks data," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 64(1), pages 69-85, February.
    4. Daniel Nevo & Reiko Nishihara & Shuji Ogino & Molin Wang, 2018. "The competing risks Cox model with auxiliary case covariates under weaker missing-at-random cause of failure," Lifetime Data Analysis: An International Journal Devoted to Statistical Methods and Applications for Time-to-Event Data, Springer, vol. 24(3), pages 425-442, July.
    5. Balakrishnan, N. & So, H.Y. & Ling, M.H., 2015. "EM algorithm for one-shot device testing with competing risks under exponential distribution," Reliability Engineering and System Safety, Elsevier, vol. 137(C), pages 129-140.

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