A0499
Title: A parsimonious joint model of survival outcomes and time-varying biomarkers
Authors: Zhiyang Zhou - University of Wisconsin-Milwaukee (United States) [presenting]
Lihui Zhao - Northwestern University (United States)
Abstract: Dynamic risk prediction dynamically updates an individual's risk assessment for a particular outcome by integrating new information over time. The core challenge of this approach involves estimating the intricate interplay between time-varying risk factors and survival outcomes. The shared-random-effects joint model, a key strategy for dynamic risk prediction, simultaneously fits submodels for longitudinal/survival outcomes. However, as the number of time-varying biomarkers increases, so does the size of unknown parameters, making the model computationally demanding. Additionally, this inflation may potentially compromise the predictive accuracy due to approximation errors in handling the complex likelihood function. To mitigate these issues, a parsimonious joint model is introduced to enhance computational efficiency. The method demonstrates competitive predictive accuracy, verified by numerical studies.
