  Clinical trials with a hybrid control arm (a control arm constructed from a
combination of randomized patients and real-world data on patients receiving
usual care in standard clinical practice) have the potential to decrease the
cost of randomized trials while increasing the proportion of trial patients
given access to novel therapeutics. However, due to stringent trial inclusion
criteria and differences in care and data quality between trials and community
practice, trial patients may have systematically different outcomes compared to
their real-world counterparts. We propose a new method for analyses of trials
with a hybrid control arm that efficiently controls bias and type I error.
Under our proposed approach, selected real-world patients are weighted by a
function of the "on-trial score," which reflects their similarity to trial
patients. In contrast to previously developed hybrid control designs that
assign the same weight to all real-world patients, our approach upweights of
real-world patients who more closely resemble randomized control patients while
dissimilar patients are discounted. Estimates of the treatment effect are
obtained via Cox proportional hazards models. We compare our approach to
existing approaches via simulations and apply these methods to a study using
electronic health record data. Our proposed method is able to control type I
error, minimize bias, and decrease variance when compared to using only trial
data in nearly all scenarios examined. Therefore, our new approach can be used
when conducting clinical trials by augmenting the standard-of-care arm with
weighted patients from the EHR to increase power without inducing bias.
