The value of real-world evidence depends on the quality of the underlying real-world data and the integrity of the analytic methods used for its generation. Therefore, demonstrating the validity and accuracy of clinical endpoints becomes important. In oncology research, mortality as a variable, and overall survival, as an endpoint, are critical, since low sensitivity in mortality surveillance is known to bias overall survival estimates.
In this study, researchers from Flatiron refreshed and expanded results obtained with a novel composite real-world mortality variable for oncology studies, generated from multiple structured and unstructured data sources. Findings using a transparent and rigorous methodology demonstrated a mortality variable with high sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) and accuracy across a wide range of cancer types, enabling the reliable analysis (with negligible bias) of overall survival as an endpoint in large real-world cohorts of patients with cancer.
Why this matters
In order to unlock the full value of real-world data, it is critical to have meaningful and reliable analytical tools, and for researchers to know, transparently, how those tools perform. The ability to analyze survival is a key aspect of oncology research that requires solid mortality surveillance sources. In clinical trials, this is achieved via dedicated follow up; in Flatiron Health EHR-derived databases, researchers have developed a composite mortality variable by aggregating multiple sources including structured EHR, unstructured EHR through abstraction, Social Security Death Index (SSDI) and obituaries. This variable has now been benchmarked against the gold standard of the National Death Index (NDI) across a wide portfolio of solid and hematologic tumor types, showing high accuracy and high sensitivity without sacrificing specificity which results in high PPV and NPV that minimizes biases and enables robust survival analyses for studies that may span from natural history of disease to comparative effectiveness.