Statistical Methods and Validation Analyses for the Integration of External Data in Clinical Trials

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Project Summary/Abstract Our research will focus on the use of external data, including previous clinical studies and real-world datasets, in the design and analysis of phase II and III oncology trials. We consider, for example, designs that include early stopping decisions based on data generated from the trial and external patient-level data. External datasets have the potential to improve final analyses and interim decisions of future single- arm and randomized clinical trials. They can also accelerate the development of new treatments, by reducing the number of patients that need to be enrolled in clinical studies and therefore their duration. However the use of external information to analyze clinical trials is currently sporadic. Indeed, the integration of external patient-level information to test new treatments can increase the risk of bias in the evaluation of experimental treatments. An effective use of external data in the design and analysis of clinical studies requires both, adequate statistical methodologies, and validation analyses, to quantify risks and potential efficiency gains compared to standard statistical plans of single-arm and randomized trial designs. We will develop novel designs to use external data in future trials. We will use collections of datasets in prostate cancer, glioblastoma, and lung cancer, including patient-level outcomes and prognostic variables. These collections are necessary to effectively use external data in clinical studies. We will then introduce and apply validation methods to evaluate statistical designs using disease-specific data collections, inclusive of clinical trials and real world data. The validation summaries that we will produce, will quantify the efficiency of trial designs and the risks of the integration of external data, associated for example, to unmeasured confounders or measurement errors on prognostic variables and outcome.