JMOptim

After the diagnosis of a disease, a key question in clinical practice is accurate prediction of patient prognosis. In this context, there is an increasing interest in joint models for longitudinal and time-to-event data due to their ability of updating estimates of conditional survival probabilities as additional longitudinal information (such as individual biomarkers measurements, possibly obtained from digital health) are recorded. The main drawback of this modelling approach, however, is of a practical nature: complex model specifications are still difficult to implement and computationally expensive to estimate, limiting their use for real time analysis. As a consequence, the usage of joint models for prognostic purposes can be burdensome.