REsearch on BrEast Cancer induced chronic conditions supported by Causal Analysis of multi-source data

The REBECCA proposal aspires to tap into the potential of Real-world Data (RWD) for supporting groundbreaking clinical research on complex chronic conditions (CCC) as a complement to Randomised Controlled Trials (RCT).

REBECCA moves beyond the analysis of Electronic health records (EHR), by combining it with detailed monitoring data from multiple wearable, online behaviour and registry data to monitor patients’ functional, emotional and Quality of Life trajectories, with high temporal granularity.

REBECCA also proposes explainable causal modelling combined with deep learning to account for observed and latent confounders in RWD analysis.

The project will focus on the complex array of chronic comorbidities developed during breast cancer recovery, in particular studying the impact of primary and adjuvant cancer treatment on patients’ quality of life and assessing the value of detailed patient monitoring as a means for improved patient care, but will also demonstrate the extensibility of REBECCA to other forms of cancer.To this end, a total of seven studies in Sweden, Norway and Spain will produce new knowledge on clinical management of cancer patients that will shape future guidelines and practices for post-cancer treatments.

Best practices resulting from the REBECCA studies will be disseminated to researchers, public health and regulatory bodies throughout Europe to facilitate wider adoption of RWD in clinical research.

In addition, the REBECCA platform, capable of detailed monitoring and privacy-preserving federated cross-country data analysis, will provide an infrastructure for continued progress on use of RWD beyond the end of the project.

Through these activities, REBECCA aims at the mass adoption of RWD for understanding CCCs and ultimately at establishing RWD as a valuable clinical research and patient management tool.