Utility of Real-World Data in Clinical Practice

Several prognostic models for lymphoid cancers, such as the follicular lymphoma international prognostic index (FLIPI), have been developed using data from clinical trials. Conversely, other models were built using a combination of clinical trial and real-world data, including the m7-FLIPI, the international prognostic score for Hodgkin lymphoma, and the National Comprehensive Cancer Network-IPI for diffuse large B-cell lymphoma. Data from real-world patients have the ability to support the utility of prognostic models in clinical practice.

Additional studies have revealed that real-world data may play a key role in assessing the risk of rare complications and related outcomes. Clinical trials are frequently unable to detect late complications or rare adverse events, especially once the primary end point has been met. Consequently, real-world data are crucial for postmarketing surveillance studies, which monitor the occurrence of adverse events following the approval of a drug.

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“Due to patient groups becoming smaller, conducting large phase 3 trials may become increasingly challenging; [thus], we need to develop new ways of gathering evidence for precision medicine in clinical practice,” Dr El-Galaly said. “Combinations of real world evidence and phase 1/2 studies may be the answer to this in some situations.”

Other Applications and Limitations

Although randomized clinical trials are used to assess the efficacy of therapy, real-world data may allow evaluation of the effectiveness of a treatment. Effectiveness is difficult to assess because of heavy reliance on postmarketing data, but it is often more meaningful to regulatory bodies and patients. Furthermore, real-world data may provide insight into patients with poor compliance, comorbidities, and reduced health literacy, all of which have been linked to worse clinical outcomes.

Despite its many strengths, the use of real-world data has potential limitations. In some cases, access restrictions may be imposed by institutional policies or legal regulations; thus, data may be missing or incomplete. For some applications, such as health technology assessment or economic modelling, the absence of key information can be a major concern. Other limitations include issues related to storage, collection, and ownership of real-world data.

“Clinicians will need to pay more attention [to] the cost-effectiveness of therapeutic interventions in the precision medicine era,” Dr El-Galaly told Hematology Advisor. “In particular, [they will need to watch out] for therapies that have not [demonstrated] survival benefit in well conducted trials.”

He concluded, “Prioritization will be key for sustainability of health care as we know it.”

Disclosures: Some authors have declared affiliations with the pharmaceutical industry. Please refer to the original study for a full list of disclosures.

Reference

1. El-Galaly TC, Cheah CY, Villa D. Real world data as a key element in precision medicine for lymphoid malignancies: potentials and pitfalls [published online May 29, 2019]. Br J Haematol. doi:10.1111/bjh.15965