Abstract
Predictive risk models are advocated in psychosocial oncology practice to provide timely and appropriate support to those likely to experience the emotional and psychological consequences of cancer and its treatments. New digital technologies mean that large scale and routine data collection are becoming part of everyday clinical practice. Using these data to try to identify those at greatest risk for late psychosocial effects of cancer is an attractive proposition in a climate of unmet need and limited resource. In this paper, we present a framework to support the development of high-quality predictive risk models in psychosocial and supportive oncology. The aim is to provide awareness and increase accessibility of best practice literature to support researchers in psychosocial and supportive care to undertake a structured evidence-based approach.