Most work on survival prediction focuses on estimating survival probabilities rather than predicting individual event times. Recent conformal methods have made it possible to construct prediction intervals for survival times with right-censored outcomes, but existing approaches are restricted to settings with covariates only measured at baseline and do not address dynamic prediction with longitudinal data. We study prediction intervals for survival times in a dynamic prediction framework with longitudinal covariates. Our approach uses Penalized Regression Calibration (PRC) as a working dynamic prediction model, combining linear mixed models for the longitudinal histories with a Cox model for post-landmark survival, and then applies a conformal calibration step to obtain prediction intervals. We compare naive intervals obtained by direct inversion of the survival function estimated by PRC to our dynamic conformal method. A Monte Carlo simulation study evaluates empirical coverage and interval length across sample sizes, censoring levels, landmark times, and non-proportional hazards (NPH) scenarios. We illustrate the proposed methodology by computing dynamic prediction intervals for the time until a dementia diagnosis in the ADNI dataset. The results show that naive inversion is often unreliable, whereas the proposed dynamic conformal method yields more stable predictive performance.

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