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Improving estimation efficiency for semi-competing risks data with partially observed terminal event

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  • Menggang Yu

Abstract

Semi-competing risks data arise when two types of events, non-terminal and terminal, may be observed. When the terminal event occurs first, it censors the non-terminal event. Otherwise the terminal event is observable after the occurrence of the non-terminal event. In practice, it can be hard to ascertain all terminal event information after the non-terminal event. Yu and Yiannoutsos [(2015), ‘Marginal and Conditional Distribution Estimation from Double-Sampled Semi-Competing Risks Data’, Scandinavian Journal of Statistics, 42, 87–103] considered a setting when the terminal event is ascertained via double sampling from only a subset of patients who experienced the non-terminal event. They discussed estimation for marginal and conditional distributions under this double sampled semi-competing risk data framework. We propose a more efficient estimation method in the same setting by fully utilising the non-terminal event information. The efficiency gain can be substantial as observed in our simulation study.

Suggested Citation

  • Menggang Yu, 2016. "Improving estimation efficiency for semi-competing risks data with partially observed terminal event," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 28(4), pages 860-874, October.
    • Handle: RePEc:taf:gnstxx:v:28:y:2016:i:4:p:860-874
    DOI: 10.1080/10485252.2016.1234051
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    References listed on IDEAS

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    1. Weijing Wang, 2003. "Estimating the association parameter for copula models under dependent censoring," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 257-273, February.
    2. Constantine E. Frangakis & Donald B. Rubin, 2001. "Addressing an Idiosyncrasy in Estimating Survival Curves Using Double Sampling in the Presence of Self-Selected Right Censoring," Biometrics, The International Biometric Society, vol. 57(2), pages 333-342, June.
    3. Constantine E. Frangakis & Donald B. Rubin, 2001. "Rejoinder to Discussions on Addressing an Idiosyncrasy in Estimating Survival Curves Using Double Sampling in the Presence of Self-Selected Right Censoring," Biometrics, The International Biometric Society, vol. 57(2), pages 351-353, June.
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    Cited by:

    1. Rui Chen & Menggang Yu, 2021. "Tailored optimal posttreatment surveillance for cancer recurrence," Biometrics, The International Biometric Society, vol. 77(3), pages 942-955, September.

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