Dynamic prediction of risk of death using history of cancer recurrences in joint frailty models.

Evaluating the prognosis of patients according to their demographic, biological, or disease characteristics is a major issue, as it may be used for guiding treatment decisions. In cancer studies, typically, more than one endpoint can be observed before death. Patients may undergo several types of events, such as local recurrences and distant metastases, with death as the terminal event. Accuracy of clinical decisions may be improved when the history of these different events is considered. Thus, it may be useful to dynamically predict patients' risk of death using recurrence history. As previously applied within the framework of joint models for longitudinal and time to event data, we propose a dynamic prediction tool based on joint frailty models. Joint modeling accounts for the dependence between recurrent events and death, by the introduction of a random effect shared by the two processes. We estimate the probability of death between the prediction time t and a horizon t + w, conditional on information available at time t. Prediction can be updated with the occurrence of a new event. We proposed and compared three prediction settings, taking into account three different information levels. The proposed tools are applied to patients diagnosed with a primary invasive breast cancer and treated with breast-conserving surgery, followed for more than 10 years in a French comprehensive cancer center.