  The incorporation of "real-world data" to supplement the analysis of trials
and improve decision-making has spurred the development of statistical
techniques to account for introduced confounding. Recently, "hybrid" methods
have been developed through which measured confounding is first attenuated via
propensity scores and unmeasured confounding is addressed through (Bayesian)
dynamic borrowing. Most efforts to date have focused on augmenting control arms
with historical controls. Here we consider augmenting treatment arms through
"expanded access", which is a pathway of non-trial access to investigational
medicine for patients with seriously debilitating or life-threatening
illnesses. Motivated by a case study on expanded access, we developed a novel
method (the ProPP) that provides a conceptually simple and easy-to-use
combination of propensity score weighting and the modified power prior. Our
weighting scheme is based on the estimation of the average treatment effect of
the patients in the trial, with the constraint that external patients cannot
receive higher weights than trial patients. The causal implications of the
weighting scheme and propensity-score integrated approaches in general are
discussed. In a simulation study our method compares favorably with existing
(hybrid) borrowing methods in terms of precision and type-I error rate. We
illustrate our method by jointly analysing individual patient data from the
trial and expanded access program for vemurafenib to treat metastatic melanoma.
Our method provides a double safeguard against prior-data conflict and forms a
straightforward addition to evidence synthesis methods of trial and real-world
(expanded access) data.
