activity
20162025
most citedThe scalable Birth-Death MCMC Algorithm for Mixed Graphical Model Learning with Application to Genomic Data Integration

1 citations · 3 across the 6 of their papers we have counts for

collaborators

6 papers

stat.AP2025

Accommodating the Analysis Model in Multiple Imputation for the Weibull Mixture Cure Model:Performance under Penalized Likelihood

Changchang Xu, Laurent Briollais, Irene L Andrulis +1

Introduction In analysis of time-to-event outcomes, a mixture cure (MC) model is preferred over a standard survival model when the sample includes individuals who will never experi…

stat.ME2020★ 1 cited

Bayesian Estimation of Two-Part Joint Models for a Longitudinal Semicontinuous Biomarker and a Terminal Event with R-INLA: Interests for Cancer Clinical Trial Evaluation

Denis Rustand, Janet van Niekerk, Håvard Rue +3

Two-part joint models for a longitudinal semicontinuous biomarker and a terminal event have been recently introduced based on frequentist estimation. The biomarker distribution is…

stat.ML2020★ 1 cited

The scalable Birth-Death MCMC Algorithm for Mixed Graphical Model Learning with Application to Genomic Data Integration

Nanwei Wang, Laurent Briollais, Helene Massam

Recent advances in biological research have seen the emergence of high-throughput technologies with numerous applications that allow the study of biological mechanisms at an unprec…

stat.AP2020

A Bayes Factor Approach with Informative Prior for Rare Genetic Variant Analysis from Next Generation Sequencing Data

Jingxiong Xu, Wei Xu, Laurent Briollais

The discovery of rare genetic variants through Next Generation Sequencing is a very challenging issue in the field of human genetics. We propose a novel region-based statistical ap…

stat.ME2020★ 1 cited

A Competing Risks Model with Binary Time Varying Covariates for Estimation of Breast Cancer Risks in BRCA1 Families

Yun-Hee Choi, Hae Jung, Saundra Buys +6

Mammographic screening and prophylactic surgery such as risk-reducing salpingo oophorectomy (RRSO) can potentially reduce breast cancer risks among mutation carriers of BRCA famili…

stat.CO2016

Analyzing Genome-wide Association Study Data with the R Package genMOSS

Matthew Friedlander, Adrian Dobra, Helene Massam +1

The R package (R Core Team (2016)) genMOSS is specifically designed for the Bayesian analysis of genome-wide association study data. The package implements the mode oriented stocha…