• Comparative and combined effectiveness of innovative therapies in cancer: A literature review

      Geldof, Tine; Rawal, Smita; Van Dyck, Walter; Huys, Isabelle (Future Medicine Ltd, 2019)
      To achieve therapeutic innovation in oncology, already expensive novel medicines are often concomitantly combined to potentially enhance effectiveness. While this aggravates the pricing problem, comparing effectiveness of novel yet expensive (concomitant) treatments is much needed for healthcare decision-making to deliver effective but affordable treatments. This study reviewed published clinical trials and real-world studies of targeted and immune therapies. In total, 48 studies compared and/or combined multiple novel products on breast, colorectal, lung and melanoma cancers. To a great extent, products evaluated in each study were owned by one manufacturer. However, cross-manufacturer assessments are also needed. Next to costs and intensive market competition, the absence of a regulatory framework enforcing real-world multiproduct studies prevents these from being conducted. Trusted third parties could facilitate such real-world studies, for which appropriate and efficient data access is needed.
    • Real-world evidence gathering in oncology: The need for a biomedical big data insight-providing federated network (Published Online)

      Geldof, Tine; Huys, Isabelle; Van Dyck, Walter (Frontiers Editorial Office, 2019)
      Moving towards new adaptive pathways for the development and access to innovative medicines implies that real-world data (RWD) collected throughout the medicinal product life cycle is becoming increasingly important. Big data analytics on RWD can obtain new and powerful insights into medicines’ effectiveness. However, the healthcare ecosystem still faces many sector-specific challenges that hamper the use of big data analytics delivering real world evidence (RWE). We distinguish between exploratory (ExTE) and hypotheses-evaluating (HETE) studies testing treatment effectiveness in the real world. From our experience and in the context of the four V’s of data management, we show that to get meaningful results data Variety and Veracity are needed regardless of the type of study conducted. More so, for ExTE studies high data Volume is needed while for HETE studies high Velocity becomes essential. Next, we highlight what are needed within the biomedical big data ecosystem, being: (a) international data reusability; (b) real-time RWD processing information systems; and (c) longitudinal RWD. Finally, in an effort to manage the four V’s whilst respecting patient privacy laws we argue for the development of an underlying federated RWD infrastructure on a common data model, capable of bringing the centrally-conducted big data analysis to the de-centrally kept biomedical data.