
Real word data could help resolve uncertainty about the effectiveness of new treatments
What counts as ‘real world data’?
The term ‘real world data’ (other related terms are ‘real world evidence’, and ‘evidence from clinical experience’) generally means anything collected outside of a classical randomised controlled trial (RCT). Data elements typically include the characteristics of patients, the treatments they receive, and the outcomes achieved. A review of definitions has found that in the spectrum of data – derived from the highly selected RCT at one end, to electronic health records of routine clinical practice at the other ‒ there is a variety of opinion on what qualifies. Definitions have been developed by various groups such as the Innovative Medicines Initiative (IMI) in its getReal programme, and the Rand Corporation, but some stakeholders have not adopted them and have not even developed their own definition. A common understanding of what ‘real world data’ means would certainly help to inform decision making when stakeholders discuss its use, say the review authors. Various types of study can contribute to real world data, though not all definitions include them all. They are:- Observational studies
- Studies that may have a protocol but investigators do not have control over aspects such as treatment assignment or selection of a study population
- ‘Pragmatic clinical trials’, which measure the benefit a treatment produces in routine clinical practice, and may be randomised.
CancerLinQ (US)

Oncology Data Network

NCRAS (UK)

ESME (France)

Dutch Cancer Treatment Registries

Flatiron – a Roche subsidiary

Disease-specific registries and studies
Responding to increasing pressure to demonstrate real world effectiveness, some pharmaceutical companies have also launched increasingly ambitious registries and registry studies in cancers where they have a particular interest. Takeda’s Insight MM on multiple myeloma is one. This global, prospective, non-interventional observational study is gathering data on the demographic and clinical characteristics of patients treated in real world settings, together with details of their therapeutic regimen, and outcomes, including patient-reported outcomes. This is a disease where a rapid expansion of treatment options over the past 20 years has greatly improved survival rates, but with very little clarity about which options work best for which patients in which disease settings, or what the optimal dose, timing, duration or combinations and sequences are. The study seeks to answer some of those questions. Lung cancer is another field that has seen a significant expansion of treatment options together with improvements in survival over the recent decades. BMS, a developer of immuno-oncology drugs, which are increasingly important in this disease, has set up I-O Optimise, which draws data from a number of real world data sources across Europe relating to characteristics, treatment patterns and clinical outcomes of more than 45,000 patients a year with non-small-cell lung cancer, small-cell lung cancer and mesothelioma. These are some of the larger examples among a vast array of disease-specific registries set up by companies, academia and even patient advocacy groups. While each of them generate data that throw light on a variety of aspects of the management of different indications, their proliferation raises questions about fragmentation and the potential to harmonise and standardise the data collected to maximise its power.No substitute for randomised trials
High-quality databases and registries are clearly needed, and if there is one thing everyone agrees on, it is that the data need to be robust. In a white paper led by the EORTC (European Organisation for Research and Treatment of Cancer) together with members of the BioMed Alliance, the authors say that it is critical to have Europe-wide population-based registries with clinical data, biological and imaging data, biomarker test results, and data on all therapies received and outcomes, but “controlling the quality and accuracy of the data is essential”, and that “minimal quality control requirements for building these databases and registries will need to be developed and implemented” (Eur Respir J 2019, 53:1801870). But as part of a ‘treatment optimisation’ manifesto, the EORTC and others are also calling for a new model of treatment development ADD LINK that better addresses the needs of patients and society (Mol Oncol 2019, 13:558–566). The way forward, the authors say, is to ramp up not just data collection but also applied clinical research, from the time treatments are authorised, which would still ensure patients receive them, with regulators and HTA agencies committed to a post-authorisation strategy. Funding of research could come jointly from healthcare systems and industry. Denis Lacombe, EORTC director general, points to EORTC’s own work, for example on the use of radiotherapy and drugs in treating glioblastoma, as the kind of practice-changing research that is needed. In their paper, the DANTE trial in the UK and STOP-GAP in Canada are mentioned – both are RCTs supported by independent funders and are looking at optimal treatment duration of immunotherapies in melanoma, which was not adequately determined during development. Lacombe expresses concerns about the limitations of initiatives like ESME and CancerLinQ, commenting that it would take an enormous effort to structure such data to iron out treatment-related uncertainties. “It depends on the purpose for assembling real world data. These projects can certainly be helpful in diagnostic, monitoring and uptake of treatment strategies. But when it comes to measuring, assessing and making decisions about treatment effect, things are much more difficult, so it is advisable that they are seen as complements to classical clinical research methodologies.”“When it comes to… making decision about treatment effect, it is advisable that real world data are seen as complements to classical clinical research”His remarks would seem to be borne out by the findings from two studies using data from the ESME database of metastatic breast cancer (MBC). On the positive side is a paper that identified improvements in survival, but only in HER2+ cases. It highlighted the unmet need in the luminal and triple negative subtypes, which may be addressed as new targeted and immunotherapies enter clinical practice (Eur J Cancer 2018, 96:17–24). However, that database was found to be insufficiently robust to answer questions about the impact of adding bevacizumab (Avastin) to chemotherapy in treating patients with HER2-negative metastatic breast cancer (Ann Oncol 2016, 27:1725‒32). ESME provided a large patient sample – about 3,400 receiving bevacizumab and paclitaxel, and 1,300 paclitaxel alone, and the bevacizumab group had significantly better overall and progression free survival. But the authors said: “Our data cannot support extension of current use of bevacizumab in MBC,” citing limitations in the data, including patient selection differences, lack of inclusion of performance status, potential bias of only being done in top centres, and possible data collection differences. As oncologist Christopher Booth at Queen’s University Cancer Research Institute, Canada, notes with colleagues in a paper, ‘Real-world data: towards achieving the achievable in cancer care,’ “…the comparison of outcomes between nonrandomised groups of patients who have received different treatments in routine practice remains problematic and is not a substitute for RCTs,” (Nat Rev Clin Oncol 2019, 16:312–25). Real world data can offer important insights, they argue, but investigators “need to move beyond simply describing gaps in care and towards designing intervention studies to improve patient care and outcomes”. The EORTC’s treatment optimisation manifesto sets out the principles. Taking this forward will be a political as well as a scientific task, and Cancer World will shortly report on progress for this optimisation agenda.
1 comment