To join this seminar virtually: Please request Zoom connection details from headsec [at] stat.ubc.ca.
Time: 11:00am – 11:30am
Speaker: Nirupama Tamvada, UBC Statistics MSc student
Title: Penalized Competing Risks Analysis using Case-base sampling
Abstract: In biomedical studies, quantifying the association of prognostic genes/markers on the time-to-event is crucial for predicting a patient's risk of disease based on their specific covariate profile. Modelling competing risks is essential in such studies, as patients may be susceptible to multiple mutually exclusive events, such as death from alternative causes. Existing methods for competing risks often yield coefficient estimates that lack interpretability, as they cannot be associated with the event rate. Moreover, the high dimensionality of genomic data, where the number of variables exceeds the number of subjects, presents a significant challenge. In this work, we propose a novel approach that involves fitting an elastic-net penalized multinomial model using the case-base sampling framework developed by Hanley and Miettinen (2009) to model competing risks survival data. Furthermore, we develop a simple, two-step method known as the de-biased case-base to enhance the prediction performance of the risk of disease. Through a comprehensive simulation study that emulates biomedical data, we show that the case-base method is competent in terms of variable selection and survival prediction, particularly in scenarios such as non-proportional hazards. We additionally showcase the flexibility of this approach in providing smooth-in-time incidence curves, which improve the accuracy of patient risk estimation.
Time: 11:30am – 12:00pm
Speaker: Yitong (Maggie) Liu, UBC Statistics MSc student
Title: Robust Sparse Covariance-Regularized Regression for High-Dimensional Data with Casewise and Cellwise Outliers
Abstract: Modern biomedical datasets, such as those found in genomic studies, often involve a large number of predictor variables relative to the number of observations, pointing to the need for statistical methods specifically designed to handle high-dimensional data. In particular, for a regression task, regularized methods are needed to select a subset of features among the large number of variables that are relevant for predicting a response. The presence of outliers in the data further complicates this task. Many existing robust and sparse regression methods are computationally expensive when the dimensionality of the data is high. Furthermore, most of these previously developed methods were developed under the assumption that outliers occur casewise, which is not always a realistic assumption in high-dimensional settings. We propose a sparse and robust regression method for high-dimensional data that is based on regularized precision matrix estimation. Our method can handle both casewise and cellwise outliers in low- and high-dimensional settings. Through simulation studies, we also compare our method to existing sparse and robust methods by evaluating computational efficiency, prediction performance, and variable selection capabilities.