Real-world data and evidence in pain À¶Ý®ÊÓƵ: an IMMPACT comprehensive review and best practice recommendations.

À¶Ý®ÊÓƵ

Abstract

Real-world data (RWD) can be defined as routinely collected clinical or administrative data that might be used for À¶Ý®ÊÓƵ purposes and to generate real-world evidence (RWE). Computerized search and data mining methods, large electronic databases, and the development of novel computational and statistical methods allow for improved access to and analysis of RWD. Although RWD afford the opportunity to generate RWE with potentially improved efficiency and generalizability over prospective clinical studies, it is important to understand and apply best practices when analysing RWD, particularly when the goal is to generate RWE of diagnostic, prognostic, or treatment effectiveness. Real-world evidence can provide evidence complementary to randomized clinical trials (RCTs), especially in scenarios where RCTs are difficult to conduct. Real-world evidence studies need to be carefully designed, the À¶Ý®ÊÓƵ question clearly defined and addressable with the available RWD source, variables (treatment, outcome, covariates) operationalized, and hypotheses and analyses specified before data access. Sound interpretation of results requires a deep understanding of the benefits and limitations of RWE studies, including often deficient data quality, confounding, and other potential sources of bias. Registered protocols, registered reports as a publishing model, and/or restricted access to data until protocols are in place can be encouraged by journals and enforced by data guardians and will contribute to the emergence of high-quality RWD studies. Here, we summarize guidance documents on generating RWE of treatment effectiveness or comparative effectiveness, discuss the strengths and limitations of RWD and RWE, and provide recommendations for conducting effectiveness RWE studies in the pain field.

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