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Nonparametric estimation with left-truncated semicompeting risks data

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  • L. Peng
  • J. P. Fine

Abstract

Nonparametric estimators for competing risks data can be applied to semicompeting risks data, a type of multi-state data where a terminating event may censor a nonterminating event, after forcing the data into the competing risks format. Complications may arise with left truncation of the terminating event, where the competing risks analysis naively truncates the nonterminating event using the left-truncation time for the terminating event, which may lead to large efficiency losses. We propose nonparametric estimators which use all semicompeting risks information and do not require artificial truncation. The uniform consistency and weak convergence of the estimators are established and variance estimators are provided. Simulation studies and an analysis of a diabetes registry demonstrate large efficiency gains over the naive estimators. Copyright 2006, Oxford University Press.

Suggested Citation

  • L. Peng & J. P. Fine, 2006. "Nonparametric estimation with left-truncated semicompeting risks data," Biometrika, Biometrika Trust, vol. 93(2), pages 367-383, June.
    • Handle: RePEc:oup:biomet:v:93:y:2006:i:2:p:367-383
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    File URL: http://hdl.handle.net/10.1093/biomet/93.2.367
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    Cited by:

    1. Huazhen Lin & Ling Zhou & Chunhong Li & Yi Li, 2014. "Semiparametric transformation models for semicompeting survival data," Biometrics, The International Biometric Society, vol. 70(3), pages 599-607, September.
    2. Zhao, XiaoBing & Zhou, Xian, 2010. "Applying copula models to individual claim loss reserving methods," Insurance: Mathematics and Economics, Elsevier, vol. 46(2), pages 290-299, April.
    3. Guibert, Quentin & Planchet, Frédéric, 2018. "Non-parametric inference of transition probabilities based on Aalen–Johansen integral estimators for acyclic multi-state models: application to LTC insurance," Insurance: Mathematics and Economics, Elsevier, vol. 82(C), pages 21-36.
    4. Ruosha Li & Limin Peng, 2011. "Quantile Regression for Left-Truncated Semicompeting Risks Data," Biometrics, The International Biometric Society, vol. 67(3), pages 701-710, September.

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