Bradford Hill Seminar - Leveraging External Data for Testing Experimental Therapies with Biomarker Interactions in Randomized Clinical Trials

All are welcome to the next Bradford Hill Seminar hosted by the MRC Biostatistic Unit. 

Title Leveraging External Data for Testing Experimental Therapies with Biomarker Interaction in Randomized Clinical Trials

Speaker: Professor Lorenzo Trippa, Harvard T.H Chan School of Public Health and Dana- Farber Cancer Institute 

Speaker bio:

Lorenzo Trippa, PhD, is a professor of biostatistics at Harvard T.H. Chan School of Public Health and Dana-Farber Cancer Institute. His research focuses on statistical methods for clinical trial design, with an emphasis on Bayesian and adaptive approaches, external controls, and precision oncology. Trippa’s work aims to improve the efficiency and reliability of trials in complex settings, particularly in cancer research, and has connects methodological research and applied clinical studies.

Abstract: 

In oncology the efficacy of novel therapeutic often differs across patient subgroup, and these variations are difficult to predict during the initial phases of the drug development process. The relation between the power of randomized clinical trials and heterogeneous treatment effect has been discussed by several authors. In particular, false negative results are likely to occur when the treatment effects concentrate in a subpopulation but the study design did not account for potential heterogeneous treatment effect. The use of external data from completed clinical studies and electronic health records has the potential to improve decision-making throughout the development of new therapeutics, from early- stage trials to registration Here we discuss the use of external data to evaluate experimental treatments with potential heterogeneous treatment effects. We introduce a permutation procedure to test, at the completion of a randomized clinical trial, the null hypothesis that the experimental therapy does not improve the primary outcomes in any subpopulation. The permutation test leverages the available external data to increase power. Also, the procedure controls the false positive rate at the desired a-level without restrictive assumptions on the external data, for example, in scenarios with unmeasured confounders, different pre-treatment patient profiles in the trial population compared to the external data, and other discrepancies between the trial and the external data. we illustrate that the permutation test is optimal according to an interpretable criteria and discuss examples based on asymptotic results and simulations, followed by a retrospective analysis of individual patient- level data from a collection of glioblastoma clinical trials. 

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