Dynamic prediction of interval-censored failure time data with longitudinal marker

A main interest in clinical practice is the prediction of patient prognosis conductive to decision making. Therefore, a relevant prediction model should be able to reflect the updated patient's condition. A joint model of longitudinal markers and time-to-event data has been widely applied to estimate the association between the risk of the event and the markers' change. The purpose of this work is to provide dynamic measures for evaluating the predictive accuracy of longitudinal markers in a context of interval-censored failure time data. We propose dynamic area under curve and Brier score reflecting incomplete data structure of interval-censored data. Simulation study compares the prediction performance of joint model and landmarking method. As a real data example, the suggested method is applied to predict the occurrence of dementia using repeatedly measured cognitive scores.