Joint modeling of longitudinal biomarkers and time-to-event outcomes provides an important framework for understanding vaccine-induced immune responses and their relationship with clinical outcomes. In vaccine trials, dropout is often assumed to be non-informative and exactly observed, which may lead to biased inference when these assumptions are violated. In this study, we extend existing joint modeling approaches by incorporating a biologically motivated nonlinear mixed-effects model for longitudinal antibody trajectories and modeling dropout under both right- and interval-censored settings. The proposed framework provides a more realistic characterization of the association between immune dynamics and dropout through shared random effects. The method is applied to data from the VAX004 HIV-1 vaccine trial. The results suggest that dropout is associated with the underlying longitudinal antibody processes through shared random effects, supporting the presence of informative dropout under the proposed joint modeling framework. This association is consistently observed across Cox right-censored, Weibull right-censored, and Weibull interval-censored specifications. For the longitudinal component, the exponential-decay model provides a substantially better representation of antibody dynamics than linear and power-law alternatives. Simulation studies demonstrate reliable parameter estimation, although Hessian-based standard errors may underestimate uncertainty for parameters associated with the nonlinear component of the model. Overall, the proposed framework provides a flexible and biologically interpretable approach for joint modeling in vaccine studies, offering improved handling of realistic dropout mechanisms and the potential for extension to more complex longitudinal and survival settings.
To join this seminar virtually, please request Zoom connection details from hr.ops@stat.ubc.ca.
Speaker's page: Location: ESB 4192 / Zoom
Event date: -
Speaker: Zhili Jiang, UBC Statistics MSc student