Building an External Control Arm for Development of a New Molecular Entity: An Application in a Recurrent Glioblastoma Trial for MDNA55

In certain indications, it is well understood that randomized controlled trials lead to slow enrollment and high differential drop-out rate in the standard of care control arm when the standard of care is undesirable to patients. This not only impacts the pace of drug development but may also render a randomized trial uninterpretable if drop-out from the control arm is common. Single-arm trials are common in such indications. An external control arm (ECA) built using a propensity score method (Rosenbaum and Rubin, Biometrika 70:41–55, 1983) from subjects outside the current trial but who meet the same eligibility criteria as the subjects of the current trial, is valuable in assessing treatment effect of a new drug that cannot be otherwise assessed with a single-arm trial, or an unintentionally under-powered and arguably biased randomized controlled trial. Propensity score methods have increasingly gained importance in observational medical research, paving the way for the use of these methods in non-randomized clinical trials. In this paper, we describe our experience with building an ECA for drug development related to regulatory activities associated with a new molecular entity (NME). Through analysis of a phase 2 study, we show how best practices from causal statistical methods can be used to build an ECA for a non-randomized drug trial—a novel application of a well-studied method. To prevent selection bias, the selection of subjects for the ECA was carried out by an independent group of statisticians who were blinded to all patient outcome data. We share lessons learnt from our regulatory interactions that provide helpful suggestions on how to advance ECA-based drug development. There has been interest in confirmatory trials that use reduced control arm randomization along with an ECA. We present a design for such a hybrid trial using a propensity score weighting method and describe how a standard propensity score analysis can be used to analyze data resulting from a composite hybrid randomized and external control arm.