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Frailty modelling approaches for semi-competing risks data

  • Il Do HaEmail author
  • Liming Xiang
  • Mengjiao Peng
  • Jong-Hyeon Jeong
  • Youngjo Lee
Article
  • 6 Downloads

Abstract

In the semi-competing risks situation where only a terminal event censors a non-terminal event, observed event times can be correlated. Recently, frailty models with an arbitrary baseline hazard have been studied for the analysis of such semi-competing risks data. However, their maximum likelihood estimator can be substantially biased in the finite samples. In this paper, we propose effective modifications to reduce such bias using the hierarchical likelihood. We also investigate the relationship between marginal and hierarchical likelihood approaches. Simulation results are provided to validate performance of the proposed method. The proposed method is illustrated through analysis of semi-competing risks data from a breast cancer study.

Keywords

Frailty models Hierarchical likelihood Marginal likelihood Modified likelihood Semi-competing risks 

Notes

Acknowledgements

Dr. Ha’s research was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (No. NRF-2015R1D1A3A01015663). Dr. Xiang’s research was supported in part by the Singapore MOE AcRF (MOE2013-T2-2-118). Dr. Jeong’s research was supported in part by National Institute of Health (NIH) grants 5-U10-CA69651-11. Dr. Lee’s research was supported by an NRF grant funded by Korea government (MEST) (No. 2011-0030810).

Supplementary material

10985_2019_9464_MOESM1_ESM.pdf (64 kb)
Supplementary material 1 (pdf 64 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Department of StatisticsPukyong National UniversityBusanSouth Korea
  2. 2.School of Physical and Mathematical SciencesNanyang Technological UniversitySingaporeSingapore
  3. 3.Department of BiostatisticsUniversity of PittsburghPittsburghUSA
  4. 4.Department of StatisticsSeoul National UniversitySeoulSouth Korea

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