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Tamvada, Nirupama

University of British Columbia

In biomedical studies, quantifying the association of prognostic genes/mark- ers 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 es- sential in such studies, as patients may be susceptible to multiple mutually exclusive events, such as death from alternative causes. Existing methods for competing risks analyses often yield coefficient estimates that lack inter- pretability, 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 multi- nomial model using the case-base sampling framework to model competing risks survival data. Furthermore, we develop a 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.

Text

2023-08-22

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/85564

Statistics

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/