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Quantitative Issues in Cancer Research Working Seminar
April 3 @ 1:00 pm - 1:50 pm
Gopal Kotecha, MBB
Doctoral Student, Department of Biostatistics, Harvard University
“Leveraging external data in the analysis of randomized controlled trials: a comparative analysis”
ABSTRACT: The use of patient level information from previous studies, registries or other external datasets can support the analysis of single arm and randomized clinical trials to evaluate and test experimental treatments. However, the use of external datasets to analyze clinical trials can also compromise the scientific validity of the results due to selection bias, study to study differences, unmeasured confounding and other distortion mechanisms. Therefore, the integration of external data in the analysis of a clinical trial requires the use of appropriate methods that can detect or mitigate the risks of bias and potential distortion mechanisms. Several methods to leverage external datasets have been proposed, such as matching procedures or random effect modelling. Different methods present distinct trade offs between risks and efficiency. We conduct a comparative analysis of statistical methods to leverage external data for the analysis of randomized clinical trials. Multiple operating characteristics are evaluated, such as power, control of false positive results and bias of the treatment effects’ estimates, across candidate statistical methods. We compare the statistical methods through a comprehensive set of simulation scenarios. We also compare the methods using a collection of datasets with patient level information from several glioblastoma studies, that includes 1388 patients, in order to provide specific recommendations for future glioblastoma trials.