Please note: This seminar has been cancelled.
Analysis
of
Prescription
Drug
Utilization
with
Beta
Regression
Models
The
healthcare
sector
in
the
U.S.
is
complex
and
is
also
a
large
sector
that
generates
about
20%
of
the
country's
gross
domestic
product.
Healthcare
analytics
has
been
used
by
researchers
and
practitioners
to
better
understand
the
industry.
In
this
paper,
we
examine
and
demonstrate
the
use
of
Beta
regression
models
to
study
the
utilization
of
brand
name
drugs
in
the
U.S.
in
order
to
understand
variability
of
brand
name
drug
utilization
across
different
areas.
The
models
are
fitted
to
public
datasets
obtained
from
the
Medicare
&
Medicaid
Services
and
the
Internal
Revenue
Service.
Integrated
Nested
Laplace
Approximation
(INLA)
is
used
to
perform
the
inference.
The
numerical
results
show
that
Beta
regression
models
are
able
to
fit
the
brand
name
drug
claim
rates
well
and
including
spatial
dependence
improves
the
performance
of
the
Beta
regression
models.
This is joint work with Professor Guojun Gan, University of Connecticut.