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Cause-specific hazard regression for competing risks data under interval censoring and left truncation

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  • Li, Chenxi

Abstract

Inference for cause-specific hazards from competing risks data under interval censoring and possible left truncation has been understudied. Aiming at this target, a penalized likelihood approach for a Cox-type proportional cause-specific hazards model is developed, and the associated asymptotic theory is discussed. Monte Carlo simulations show that the approach performs very well for moderate sample sizes. An application to a longitudinal study of dementia illustrates the practical utility of the method. In the application, the age-specific hazards of AD, other dementia and death without dementia are estimated, and risk factors of all competing risks are studied.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:104:y:2016:i:c:p:197-208
    DOI: 10.1016/j.csda.2016.07.003
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    References listed on IDEAS

    as
    1. Chenxi Li & Jason P. Fine, 2013. "Smoothed nonparametric estimation for current status competing risks data," Biometrika, Biometrika Trust, vol. 100(1), pages 173-187.
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