PharmaceuticalOutsourcingQ42020 - 48

CLINICAL TRIALS

Preserving the Integrity of
Ongoing Clinical Trials in
Challenging Times
Karen Ooms
Executive Vice President and Head of Statistics
Quanticate
The COVID-19 pandemic is an unparalleled global event that
caught all businesses off-guard. This is especially prevalent in the
pharmaceutical industry as on-going clinical trials are disrupted
due to lockdown measures and social distancing requirements. In
many existing trials, subjects simply cannot get to site, visits are
being missed, data are not being recorded and clinical monitoring
is challenged as Clinical Research Associates (CRAs) experience
travel restrictions.
Members of the statistical consultancy team from CRO Quanticate
outline the three core principles to the conduct of clinical trials which
can be considered to overcome these challenges.

Sample Size Calculations
Even under normal circumstances, getting the sample size right is
the foundation that ensures a successful study while minimizing the
number of patients undergoing potentially invasive study procedures
and curtailing costs for the sponsor.
In studies where recruitment is ongoing, the pandemic has and
will likely slow enrolment as patients' movements and access to
healthcare facilities continues to be limited. In an ideal world sponsors
might think of continuing as planned until normality is restored and
procedures restart as before. However, shareholders and investors
might disagree: the costs of running a study that doesn't deliver
study results in the originally agreed timelines can be overwhelming
for major pharma companies and simply a killer for smaller biotech
companies. So, what can be done?
One potential solution is to investigate the impact on study power if
recruitment was stopped and only patients currently recruited were
allowed to continue and complete the study.
Such an exercise is similar to what is often done while planning
the study. Sponsors and their partners evaluate the impact of
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assumptions (e.g. comparator arm response level, variability, etc),
with the difference that new information obtained from external
sources might allow to reassess study design features such as power
and sample size in the light of this new knowledge. Visualize a new
study where unblinded results had only been made available after
the present study protocol was finalized. If the study suggested that
the response in the comparator arm was in fact different from what
was originally assumed, the current sample size might still allow
sufficient power to detect a clinically relevant effect.
While this situation is not a very common one, re-evaluating study
power in a large number of scenarios (either via closed formulas or
simulations) will provide the study team with a better understanding
of their next actions. However, if the only scenario to allow a sufficient
power to be achieved requires a treatment effect twice as large as
originally planned it might be worth considering if the study can
continue with the same characteristics as before the pandemic.
This example brings up an important item - the extent to which study
design itself can be altered to respond to the currently evolving
scenario. For example, consider a study where it was planned to enroll
200 patients to demonstrate a difference in a continuous outcome
between treatments ≥ 3. Assuming that the mean response was 10 in
the treatment arm and 5 in the comparator arm with an 80% power
(standard deviation = 5 and a one-sided test at a 2.5% level, dropout rate assumed to be 0% for simplicity). If no further information
on the potential treatment effect has arisen from external sources, it
is clear that if only 150 patients have currently been recruited it is not
possible to halt recruitment now because this would leave only 68%
power and increase the chances of the study being a failure.
In this situation, a viable option is to amend the study protocol to
include an unblinded (and previously unplanned) interim analysis.
The main purpose here is to estimate the current treatment effect
and deriving measures of future study success (i.e. conditional power
| October/November/December 2020

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