  After the diagnosis of a disease, one major objective is to predict
cumulative probabilities of events such as clinical relapse or death from the
individual information collected up to a prediction time, including usually
biomarker repeated measurements. Several competing estimators have been
proposed to calculate these individual dynamic predictions, mainly from two
approaches: joint modelling and landmarking. These approaches differ by the
information used, the model assumptions and the complexity of the computational
procedures. It is essential to properly validate the estimators derived from
joint models and landmark models, quantify their variability and compare them
in order to provide key elements for the development and use of individual
dynamic predictions in clinical follow-up of patients. Motivated by the
prediction of two competing causes of progression of prostate cancer from the
history of prostate-specific antigen, we conducted an in-depth simulation study
to validate and compare the dynamic predictions derived from these two methods.
Specifically, we formally defined the quantity to estimate and its estimators,
proposed techniques to assess the uncertainty around predictions and validated
them. We also compared the individual dynamic predictions derived from joint
models and landmark models in terms of prediction error, discriminatory power,
efficiency and robustness to model assumptions. We show that these prediction
tools should be handled with care, in particular by properly specifying models
and estimators.
