Dynamic Predictions and Prospective Accuracy in Joint Models for Longitudinal and Time-to-Event Data

TLDR

Joint modeling of longitudinal and time‑to‑event data has emerged as a key biostatistical approach for studying how repeatedly measured markers relate to event timing in longitudinal studies. This study evaluates the predictive performance of a longitudinal marker for a time‑to‑event outcome within the joint modeling framework. Survival probabilities for future subjects are estimated from their longitudinal measurements using a fitted joint model, and accuracy metrics are derived to quantify predictive ability. The derived accuracy measures show that the marker discriminates well between patients who will experience the event within a clinically relevant period and those who will not.

Abstract

Summary In longitudinal studies it is often of interest to investigate how a marker that is repeatedly measured in time is associated with a time to an event of interest. This type of research question has given rise to a rapidly developing field of biostatistics research that deals with the joint modeling of longitudinal and time-to-event data. In this article, we consider this modeling framework and focus particularly on the assessment of the predictive ability of the longitudinal marker for the time-to-event outcome. In particular, we start by presenting how survival probabilities can be estimated for future subjects based on their available longitudinal measurements and a fitted joint model. Following we derive accuracy measures under the joint modeling framework and assess how well the marker is capable of discriminating between subjects who experience the event within a medically meaningful time frame from subjects who do not. We illustrate our proposals on a real data set on human immunodeficiency virus infected patients for which we are interested in predicting the time-to-death using their longitudinal CD4 cell count measurements.