Statistical Methods for Cancer Risk Prediction Models - CGSI 2023

Explore statistical methods for cancer risk prediction models in this comprehensive lecture from the Computational Genomics Summer Institute. Delve into Bayesian estimation techniques for semiparametric recurrent event models, with applications to penetrance estimation in Li-Fraumeni syndrome. Examine the development of pedigree-based prediction models for identifying carriers of deleterious de novo mutations in families affected by Li-Fraumeni syndrome. Investigate personalized risk prediction approaches for cancer survivors using Bayesian semi-parametric recurrent event models with competing outcomes. Gain insights into cutting-edge statistical methodologies applied to cancer genomics and risk assessment through the discussion of related research papers and their findings.

bioRxiv. 2023 Mar 23.02.28.530537. doi: 10.1101/2023.02.28.530537. Preprint.