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Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni syndrome.

Authors
  • Shin, Seung Jun1
  • Li, Jialu2
  • Ning, Jing3
  • Bojadzieva, Jasmina4
  • Strong, Louise C4
  • Wang, Wenyi5
  • 1 Department of Statistics, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul, South Korea. , (North Korea)
  • 2 Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Pressler St, Houston, TX, USA.
  • 3 Department of Biostatistics, University of Texas MD Anderson Cancer Center, Pressler St, Houston, TX, USA.
  • 4 Department of Genetics, University of Texas MD Anderson Cancer Center, Pressler St, Houston, TX, USA.
  • 5 Department of Bioinformatics and Computational Biology, University of Texas MD Anderson Cancer Center, Houston, Pressler St, TX, USA.
Type
Published Article
Journal
Biostatistics (Oxford, England)
Publication Date
Jul 01, 2020
Volume
21
Issue
3
Pages
467–482
Identifiers
DOI: 10.1093/biostatistics/kxy066
PMID: 30445420
Source
Medline
Keywords
Language
English
License
Unknown

Abstract

A common phenomenon in cancer syndromes is for an individual to have multiple primary cancers (MPC) at different sites during his/her lifetime. Patients with Li-Fraumeni syndrome (LFS), a rare pediatric cancer syndrome mainly caused by germline TP53 mutations, are known to have a higher probability of developing a second primary cancer than those with other cancer syndromes. In this context, it is desirable to model the development of MPC to enable better clinical management of LFS. Here, we propose a Bayesian recurrent event model based on a non-homogeneous Poisson process in order to obtain penetrance estimates for MPC related to LFS. We employed a familywise likelihood that facilitates using genetic information inherited through the family pedigree and properly adjusted for the ascertainment bias that was inevitable in studies of rare diseases by using an inverse probability weighting scheme. We applied the proposed method to data on LFS, using a family cohort collected through pediatric sarcoma patients at MD Anderson Cancer Center from 1944 to 1982. Both internal and external validation studies showed that the proposed model provides reliable penetrance estimates for MPC in LFS, which, to the best of our knowledge, have not been reported in the LFS literature. © The Author 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: [email protected]

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