ArticleBiostatistics (Oxford, England)2020
Bayesian estimation of a semiparametric recurrent event model with applications to the penetrance estimation of multiple primary cancers in Li-Fraumeni syndrome.
Article in Biostatistics (Oxford, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed, 9 citations in OpenAlex.
- Performance of LFSPRO prediction in TP53 mutation status for prospectively collected probands.American journal of human genetics · 2026Article
- PERSONALIZED RISK PREDICTION FOR CANCER SURVIVORS: A GENERALIZED BAYESIAN SEMI-PARAMETRIC MODEL OF RECURRENT EVENTS WITH COMPETING OUTCOMES.The annals of applied statistics · 2025Article
- Validating Risk Prediction Models for Multiple Primaries and Competing Cancer Outcomes in Families With Li-Fraumeni Syndrome Using Clinically Ascertained Data.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2024Article
- LFSPROShiny: An Interactive R/Shiny App for Prediction and Visualization of Cancer Risks in Families With Deleterious GermlineJCO clinical cancer informatics · 2024Article
- LFSPROShiny: an interactive R/Shiny app for prediction and visualization of cancer risks in families with deleterious germlinemedRxiv : the preprint server for health sciences · 2023Article
- Article
- Penetrance Estimates Over Time to First and Second Primary Cancer Diagnosis in Families with Li-Fraumeni Syndrome: A Single Institution Perspective.Cancer research · 2020Article
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Authors and funding
6 authors at 2 institutions in 2 countries.
Funding
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.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.