Technological advances have greatly increased our understanding of the molecular basis of systemic diseases, tumor progression and treatment response. Over the last 10 years these advances have led to the identification of numerous biomarkers. These biomarkers can be divided into two types. The presence or absence of a prognostic marker can be useful for the selection of patients for treatment but does not directly predict the response to a treatment. Predictive markers aim to objectively evaluate the likelihood of benefit from a specific clinical intervention, or the differential outcomes of two or more interventions, including toxicity. Increasingly, clinicians need to interpret molecular biomarkers and understanding the technologies that underlie them in order to make treatment decisions.
Recent statistical methods have been specifically developed to study the impact of the trajectory of a biomarker, i.e., of a biomarker assessed repeatedly over time, on the risk of a clinical event. For example, joint models which combine a linear mixed (sub-)model (for the longitudinal biomarker) and a survival (sub-)model (for the time-to-event) are an interesting approach to improve prediction of progression/death using a longitudinal biomarker. Time-dependent ROC curve analysis has also been proposed to improve diagnostic accuracy, i.e., the ability of a biomarker to discriminate between healthy and diseased individuals, considering a disease status which may change over time.
In this Special Issue, experts we will focus on approaches using biomarkers (with repeated measurements or not) to predict the occurrence of a time-dependent event such as the disease (diagnosis), or toxicities (prediction), or death (prognostic).