Fast standard error estimation for joint models of longitudinal and time-to-event data based on stochastic EM algorithms.

ABSTRACT: Maximum likelihood inference can often become computationally intensive when performing joint modeling of longitudinal and time-to-event data, due to the intractable integrals in the joint likelihood function. The computational challenges escalate further when modeling HIV-1 viral load data, owing to the nonlinear trajectories and the presence of left-censored data resulting from the assay's lower limit of quantification. In this paper, for a joint model comprising a nonlinear mixed-effect model and a Cox Proportional Hazards model, we develop a computationally efficient Stochastic EM (StEM) algorithm for parameter estimation. Furthermore, we propose a novel technique for fast standard error estimation, which directly estimates standard errors from the results of StEM iterations and is bro

SUBMITTER: Yu T 

PROVIDER: S-EPMC11823262 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature