A0217
Title: Variable selection in a joint model for Huntington's disease data
Authors: Rajan Shankar - University of Sydney (Australia) [presenting]
Tanya Garcia - University of North Carolina at Chapel Hill (United States)
Garth Tarr - University of Sydney (Australia)
Abstract: Huntington's disease is a neurodegenerative disease caused by a defective Huntingtin gene, with symptoms that progressively worsen and eventually lead to a clinical diagnosis. Identifying the clinical and demographic factors that influence symptom severity and time-to-diagnosis is critical for understanding disease progression so that early intervention strategies can be timely implemented. A joint model is proposed to relate symptom severity $y$ and time-to-diagnosis $x$, conditional on clinical and demographic predictor variables $\mathbf{z}$. However, it may be that certain predictor variables are important for $y$ but not for $x$ and vice-versa, so regularisation techniques are used to select different sets of predictor variables for $y$ and $x$. Since $x$ is a time-to-event variable, there is the added challenge that many of its values are right-censored due to individuals who did not develop the disease during the study. Therefore, to fit the joint model, the expectation-maximization (EM) algorithm is applied to alternate between parameter estimation and imputation of the right-censored values until convergence. The method is demonstrated on Huntington's disease patient data, showcasing how users can choose appropriate values for the regularization tuning parameters. It not only advances statistical methods for joint modeling with censored data but also provides actionable insights into Huntington's disease progression.
