Bayesian Utility-Based Toxicity Probability Interval Design for Dose Finding in Phase I/II Trials
Molecularly
targeted
agents
and
immunotherapy
have
revolutionized
modern
cancer
treatment.
Unlike
chemotherapy,
the
maximum
tolerated
dose
of
the
targeted
therapies
may
not
pose
significant
clinical
benefit
over
the
lower
doses.
By
simultaneously
considering
both
binary
toxicity
and
efficacy
endpoints,
phase
I/II
trials
can
identify
a
better
dose
for
subsequent
phase
II
trials
than
traditional
phase
I
trials
in
terms
of
efficacy-toxicity
tradeoff.
Existing
phase
I/II
dose-finding
methods
are
model-based
or
need
to
pre-specify
many
design
parameters,
which
makes
them
difficult
to
implement
in
practice.
To
strengthen
and
simplify
the
current
practice
of
phase
I/II
trials,
we
propose
a
utility-based
toxicity
probability
interval
(uTPI)
design
for
finding
the
optimal
biological
dose
(OBD)
where
binary
toxicity
and
efficacy
endpoints
are
observed.
The
uTPI
design
is
model-assisted
in
nature,
simply
modeling
the
utility
outcomes
observed
at
the
current
dose
level
based
on
a
quasibinomial
likelihood.
Toxicity
probability
intervals
are
used
to
screen
out
overly
toxic
dose
levels,
and
then
the
dose
escalation/de-escalation
decisions
are
made
adaptively
by
comparing
the
posterior
utility
distributions
of
the
adjacent
levels
of
the
current
dose.
The
uTPI
design
is
flexible
in
accommodating
various
utility
functions
while
only
needs
minimum
design
parameters.
A
prominent
feature
of
the
uTPI
design
is
that
it
has
a
simple
decision
structure
such
that
a
concise
dose-assignment
decision
table
can
be
calculated
before
the
start
of
trial
and
be
used
throughout
the
trial,
which
greatly
simplifies
practical
implementation
of
the
design.
Extensive
simulation
studies
demonstrate
that
the
proposed
uTPI
design
yields
desirable
as
well
as
robust
performance
under
various
scenarios.
This
talk
is
based
on
the
joint
work
with
Ruitao
Lin
and
Ying
Yuan
at
MD
Anderson
Cancer
Center.