To join this seminar virtually: Please request Zoom connection details from ea [at] stat.ubc.ca.

Presentation 1

Time: 11:00am – 11:30am

Speaker: Charlotte Edgar, UBC Statistics MSc student

Title: Cellwise Robust Covariance-Regularized Regression for High-Dimensional Data

Abstract: It is common to use regularization methods when dealing with high-dimensional regression problems. The scout family, developed by Witten and Tibshirani in 2009, is a class of covariance-regularized regression procedures suitable for prediction in high-dimensional settings. The scout procedure estimates the inverse covariance matrix through two log-likelihood maximization steps that each allow for regularization and then uses the estimated inverse covariance matrix to obtain estimates of the regression coefficients. The aim of this project was to make the scout procedure robust to cellwise outliers. Cellwise outliers are common in high-dimensional datasets and recent work has led to cellwise robust covariance estimates that could be used in the scout procedure. We assess the predictive performance of robust plug-in estimators and outlier detection methods. The development of a regression method that is robust to cellwise outliers, encourages sparsity, and can be applied in high-dimensional settings would be valuable to many fields, such as genomics, and is an area undergoing current research.

Presentation 2

Time: 11:30am – 12:00pm

Speaker: Graeme Kempf, UBC Statistics MSc student

Title: The impact of disease-modifying drugs for multiple sclerosis on hospitalizations and mortality in British Columbia: A retrospective study using an illness-death multi-state model

Abstract: The efficacy of disease-modifying drugs (DMDs) for multiple sclerosis was established in clinical trials that were short and excluded older individuals and individuals living with comorbidities. This has led to a lack of knowledge of the effects of chronic DMD use and the effects of DMDs on individuals that do not meet the traditional eligibility criteria for clinical trials. Multi-state models are a technique which can advance the understanding of a disease beyond that offered by time-to-event models alone. The long-term, real-world efficacy of DMDs was explored by applying a multi-state model to administrative healthcare data. Whether exposure to any DMD is associated with fewer hospitalizations, shorter hospitalizations, and/or a reduction in the chance of dying inside or outside the hospital was investigated using multi-state techniques such as intensity-based analysis and pseudo-value regression.