GPU Accelerated Estimation of a Shared Random Effect Joint Model for Dynamic Prediction.

ABSTRACT: In longitudinal cohort studies, it is often of interest to predict the risk of a terminal clinical event using longitudinal predictor data among subjects at risk by the time of the prediction. The at-risk population changes over time; so does the association between predictors and the outcome, as well as the accumulating longitudinal predictor history. The dynamic nature of this prediction problem has received increasing interest in the literature, but computation often poses a challenge. The widely used joint model of longitudinal and survival data often comes with intensive computation and excessive model fitting time, due to numerical optimization and the analytically intractable high-dimensional integral in the likelihood function. This problem is exacerbated when the model is fit to a

SUBMITTER: Wang S 

PROVIDER: S-EPMC11384271 | biostudies-literature | 2022 Oct

REPOSITORIES: biostudies-literature