Hello, and
welcome
to
our
presentation,
Approaches
to
Comparations
with
JMP® 17.
My
name
is
Mark
Bailey.
I'm
a
senior
Analytics
Software
Tester,
and
my
co-
presenter
today
is
Jianfeng
Ding,
a
Senior
Research
Statistician
Developer.
Before
we
get
into
the
new
features,
we're
going
to
take
a
moment
to
make
sure
that
everyone
has
the
proper
background
to
appreciate
these
new
methods.
This
has
to
do
with
using
statistical
inference
when
we're
trying
to
compare
two
populations.
This
is
a
very
common
task,
and
the
comparison
usually
leads
to
a
decision
between
two
ideas
about
these
populations.
If
we
could
observe
the
populations
in
their
entirety,
we
wouldn't
need
statistics,
but
that's
not
usually
the
case.
So, we
have
to
work
with
samples
from
the
populations.
Statistical
inference
can
provide
some
really
valuable
information
about
those
samples.
In
particular,
is
there
sufficient
evidence
to
reject
one
idea
about
the
two
populations?
So
a
clear
statement
of
these
ideas
or
hypotheses
is
essential
to
making
the
correct
choice
for
the
test
and
also
for
the
correct
interpretation.
So
let's
talk
a
little
bit
about
the
ideas
or
hypotheses
that
are
part
of
these
statistical
tests.
The
alternative
and
null
hypotheses,
as
they're
known,
represent
mutually
exclusive
statements
about
these
populations,
and
no
other
hypothesis
is
possible.
For
example,
one
statement
might
be
for
population
A
and
population
B
that
their
means
are
equal.
The
other
idea
is
that
they're
not
equal.
Those
two
ideas
or
hypotheses
are
mutually
exclusive
and
no
other
hypothesis
is
possible.
What's
the
role
of
these
two
ideas?
The
alternative
hypothesis
states
the
conclusion
that
we
would
like
to
claim.
It
represents
the
populations
and
it
will
require
sufficient
evidence
in
the
data
to
overthrow
the
other
hypothesis.
The
other
one
is
called
the
null
hypothesis.
It
states
the
opposing
conclusion
that
must
be
overcome
by
strong
evidence.
It
serves
as
a
reference
for
this
comparison.
It's
assumed
to
be
true.
Now,
currently,
there's
somewhat
of
a
misunderstanding
in
hypothesis
testing
in
that
it
only
thinks
about
comparison
in
one
direction.
That's
because
historically,
this
is
the
way
it
was
presented
in
training.
That
most
often
taught
test
is
used
to
demonstrate
that
there's
a
difference
between
two
populations.
The
resulting
lack
of
understanding
can
lead
to
a
misuse
of
these
tests.
To
be
clear,
the
choice
of
the
test
is
not
a
matter
of
what
data
is
collected
or
how
the
data
is
collected.
It's
entirely
about
the
stated
hypotheses
for
the
purpose
of
your
comparison.
Let's
look
at
these
two
possibilities.
Let's
say
that
the
goal
of
our
comparison
is
to
demonstrate
a
difference.
We
want
to
explicitly
state
these
ideas
to
make
sure
the
test
is
clear.
In
the
first
example,
I
want
to
demonstrate
that
a
temperature
change
causes
a
new
outcome.
That
is,
there
is
a
difference.
We'd
like
to
claim
that
the
new
level
of
the
response
will
result
from
a
change
in
the
process
temperature.
Perhaps
we
expect
a
higher
yield
or
we'd
like
to
show
more
stability.
A
design
experiment
is
used
to
randomly
sample
from
the
population
for
a
low-
temperature
condition
and
from
a
population
of
a
high-
temperature
condition.
The
two
hypotheses
for
this
test,
the
null
hypothesis
states
that
the
temperature
does
not
affect
the
outcome.
Remember,
it's
our
reference
and
we
assume
it
to
be
true.
The
alternative
hypothesis
is
our
idea.
Temperature
affects
the
outcome,
but
we
can
decide
this
only
if
the
evidence
is
strong
enough
to
reject
the
null
hypothesis.
Now,
let's
change
that.
Let's
reverse
that.
Let's
say
that
in
this
comparison,
I
want
to
demonstrate
equivalence.
In
the
second
example,
I
want
to
show
that
a
temperature
change
does
not
cause
a
change
in
the
outcome.
That
is,
the
outcome,
either
way,
is
equivalent.
We
want
to
claim
that
a
planned
change
in
the
process
temperature
will
improve
the
yield
but
not
affect
the
level
of
an
impurity
of
the
product.
We
design
the
same
experiment,
collect
the
same
samples,
but
now
our
hypotheses
are
switched.
The
null
hypothesis
is
that
the
temperature
affects
the
outcome.
It's
our
new
reference,
we
still
assume
it
to
be
true,
while
the
alternative
hypothesis
states
that
the
temperature
does
not
affect
the
outcome,
the
impurity
level.
But
we
can
make
that
claim
only
if
the
evidence
is
strong
enough
to
reject
the
null.
So do
I
test
for
a
difference
or
for
equivalence?
The
key
is
how
you
state
your
hypotheses.
These
two
examples,
I
think
you
can
see,
use
identical
data
but
different
tests.
The
choice
of
the
test
is
not
about
the
data.
It's
about
the
claim
that
we
want
to
make
and
how
we
state
that
properly
in
the
hypothesis.
Remember
that
statistical
tests
are
unidirectional.
That
is,
we
can
reject
a
null
or
not.
If
the
test
is
to
reject
a
null
hypothesis
with
a
high
probability
when
it's
false.
Now,
let's
get
to
the
new
features
in
JMP® 17.
Jianfeng
will
now
present
the
equivalence
tests, and
when
she's
finished,
I'll
present
method
comparison.
Now,
I'm
going
to
share
my
screen.
Hello,
my
name
is
Jianfeng Ding.
I'm
a
Research
Statistic
Developer
at
JMP® 17.
In
this
video,
I'm
going
to
talk
about
the
Equivalence,
Noninferiority,
and
Superiority
Test
in
JMP® 17.
The
basic
hypothesis
test
on
the
left
is
a
test
that
most
quality
professionals
are
familiar
with.
It
is
often
used
to
compare
two
or
more
groups
of
data
to
determine
whether
they
are
statistically
different.
The
parameter
theta
can
be
a
mean
response
for
continuous
outcome
and
a
proportion
when
the
outcome
variable
is
binary.
Theta
T
represents
response
from
treatment
group
and
theta
zero
represents
the
response
from
a
control
group.
There
are
three
types
of
the
basic
hypothesis
test.
The
first
one
is
two-sided
test
and
the
rest
are
one-
sided
tests.
If
you
look
at
the
two-sided
test
on
the
left,
the
no
hypothesis
is
that
the
treatment
means
are
same
and
the
alternative
hypothesis
is
that
the
treatment
means
are
different.
Sometimes
we
really
need
to
establish
that
things
are
substantially
the
same,
and
the
machinery
to
do
that
is
called
an
equivalence
test.
An
equivalent
test
is
to
show
the
difference
in
theta
T,
theta
zero
is
within
a
pre-
specified
margin
delta
and
allow
us
to
conclude
equivalence
with
a
specified
confidence
level.
If
you
look
at
the
equivalence
test,
the
no
hypothesis
is
that
the
treatment
means
are
different
and
the
alternative
hypothesis
is
that
the
treatment
means
are
within
a
fixed
delta
of
one
another.
This
is
different
from
the
two-
sided
hypothesis
test
on
the
left.
Another
alternative
testing
scenario
is
the
noninferiority
test
which
aims
to
demonstrate
that
results
are
not
substantially
worse.
There
is
also
a
testing
scenario
called
superiority
testing
that
is
similar
to
noninferiority
testing,
except
that
the
goal
is
to
demonstrate
that
results
are
substantially
better.
There
are
five
different
types
of
equivalence
type
tests.
Depend
on
the
situation.
When
should
we
use
this
test?
Will
be
discussed
next.
These
tests
are
very
important
in
industry,
especially
in
the
biotech
and
pharmaceutical
industry.
Here
are
some
examples.
If
the
goal
is
to
show
that
the
new
treatment
does
not
differ
significantly
from
the
standard
by
more
than
some
small
margin,
then
equivalence
test
should
be
used.
For
example,
a
generic
drug
that
is
less
expensive
and
cause
few
side
effects
than
a
popular
name-
brand
drug.
You
would
like
to
prove
it
has
same
efficacy
as
the
name-
branded
one.
The
typical
goal
in
noninferiority
testing
is
to
conclude
that
a
new
treatment
process
or
product
is
not
significantly
worse
than
the
standard
one.
For
example,
a
new
manufacturing
process is
faster.
You
would
make
sure
it
creates
no
more
product
defects
than
the
standard
process.
A
superiority
test
try
to
prove
that
the
new
treatment
is
substantially better
than
the
standard
one.
For
example,
a
new
fertilizer
has
been
developed
with
several
improvements.
The
researchers
want
to
show
that
the
new
fertilizer
is
better
than
the
current
fertilizer.
How
to
set
up
the
hypothesis?
The
graph
on
the
left
summarizes
five
different
type
of
equivalence
type
tests
very
nicely.
This
graph
is
created
by
our
SAS/STAT®
colleagues,
John
Castelloe,
and
Donna
Watts.
You
can
find
their
white
paper
on
this
on
the
web.
Choosing
which
test
depend
on
the
situation.
For
each
of
this
situation,
the
region
that
we
are
trying
to
establish
with
the
test
is
shown
in
blue.
For
equivalence
analysis,
you
can
construct
an
equivalence
region
with
upper
bound
theta
zero
plus
delta
and
lower
bound
theta
zero
minus
delta.
You
can
conduct
an
equivalence
test
by
checking
whether
the
confidence
interval
of
theta
lies
in
entirely
in
the
blue
equivalence
region.
Likewise,
you
can
conduct
a
noninferiority
test
by
checking
whether
the
confidence
interval
of
theta
lies
entirely
above
the
lower
bound
if
larger
theta
is
better
or
below
the
upper
bound
if
smaller
theta
is
better.
These
tests
are
available
in
JMP® 17 in
one
way
for
comparing
normal
means
and
in
contingency
for
comparing
response
rates.
The
graphical
user
interface
of
equivalence
test
launch
dialogs
makes
it
easy
for
you
to
find
the
type
of
test
that
correspond
what
you
are
trying
to
establish.
A
forest
plot
in
the
report
summarize
the
comparison
very
nicely
and
makes
it
easy
for
you
to
interpret
the
results.
Next,
I'm
going
to
do
a
demo
about
equivalent
test,
superiority
test,
and
noninferiority
tests.
I'm
going
to
use
a
data
set
called
the
Drug
Measurement
that
is
in
the
JMP
sample
data
library.
Twelve
different
subjects
were
given
three
different
drugs
A,
B,
and
C,
and
continuous
measurement
were
made.
I
first
launched
Fit
Y
by
X
and
put
the
measurement
as
Y
and
drug
type
as
X
factor.
This
will
bring
the
one-
way
analysis.
Under
the
red
triangle
menu,
let's
first
find
equivalent
test
and
there
are
the
two
options,
means,
and
standard
deviations.
We're
going
to
focus
on
means
for
this
example.
This
will
bring
the
equivalence
launch
dialog.
In
this
section,
you
can
choose
which
test
you
would
like
to
conduct,
and
the
graph
represents
the
choice
of
the
selected
test.
For
superiority
and
noninferiority
test,
there
are
two
scenarios.
One
is
large
difference
is
better.
Another
is
small
difference
is
better.
Choose
which
one
depend
on
the
situation.
You
need
to
specify
the
delta
or
the
margin
for
the
test.
You
also
need
to
specify
the
confidence
level
alpha
for
the
test.
You
can
either
choose
the
Pooled
Variance
or
Unequaled
variance
to
run
the
test.
For
this
example,
we
run
the
equivalent
test
first
and
we
specify
three
as
a
difference.
We
are
going
to
do
the
equivalent
test
for
all
the
pairs.
Click
okay
and
it
will
bring
the
equivalent
test
result.
On
the
top,
it
is
the
statistical
detail
for
the
equivalence
test,
and
the
bottom
it's
a
forest
plot.
You
notice
there
are
two
regions.
The
blue
region
are
the
equivalence
region
and
the
red
are
the
non-
equivalence
region.
The
lines
here
represent
the
confidence
interval
of
the
mean
difference
between
two
groups.
If
we
look
at
this
line,
this
is
the
confidence
interval
of
the
mean
difference
between
drug
A
and
C,
and
you
see
this
line
is
completely
contained
inside
this
blue
region.
We
look
at
the
P
value
of
the
equivalence
test
that
is 0.02
which
is
smaller
than 0.05.
So at
this
5
%
significance
level,
we
can
declare
that
drug
A
and
C
are
equivalent.
But
when
you
look
at
the
confidence
interval
of
a
mean
difference
between
drug
A
and
B,
and
drug
B
and
C,
they
are
lined
beyond
this
blue
region.
So we
conclude
at
the
5
%
significance
level
drug
A
and
B,
and
drug
B
and
C,
we
cannot
conclude
they
are
equivalent.
Next,
if
we
assume
drug
C
is
a
standard
drug
and
you
would
like
to
find
out
if
drug
A
and
B
are
better
than
drug
C,
that
make
us
want
to
do
a
superiority
test.
Let
me
close
this
outline
note
for
now
and
we
launch
the
equivalent
test
again
and
we
click
Means.
This
time
we're
going
to
run
superiority
test.
We
click
the
superiority
test
and
we
prefer
large
difference
and
we
specify
0.4
as
our
margin.
This
time
we
need
to
set
drug
C
as
our
control
group
and
click
OK.
This
will
bring
the
superiority
test.
From
this
forest
plot
you
can
see
the
confidence
interval
of
a
mean
difference
between
drug
B
and
C
is
completely
contained
inside
this
blue
region.
So
we
conclude
that
at
the
5
%
confidence
level,
we
can
declare
that
the
B
is
superior
to
drug
C.
But
we
cannot
make
a
same
conclusion
to
drug
A
and
C.
This
concludes
my
first
example.
The
next
example
will
show
how
to
conduct a
noninferiority
test
for
the
relative
risk
between
two
proportions.
Let
me
open
the
data
table.
A
randomized
trial
is
to
compare
drug
FIDAX
as
an
alternative
to
drug
VENCO
for
the
treatment
of
colon
infections.
The
two
drugs
have
similar
efficacy
and
safety.
Two
hundred
twenty-
one
out
of
225
patients
treated
with
FIDAX
achieved
clinical
Q
by
the
end
of
the
study,
compared
to
223 out
of
257
patients
treated
with
VENCO.
We
launch
Fit
Y
by
X
again
and
we
plug
in
Q
as
Y
and
drug
as
X
factor and
account
as
a
frequency
and
click
OK.
Will
bring
the
contingency
analysis.
From
the
Likelihood
Ratio
test, Pearson
test,
and
Fisher's
Exactly
test,
they
all
indicate
that
there
is
no
big
difference
between
these
two
drugs.
But
we
would
like
to
find
out
if
drug
FIDAX
is
not
inferior
to
drug
VENCO.
We
go
to
the
up
red
triangle
and
bring
the
equivalent
test.
There
are
two
options.
One
is
the
risk
difference
and
one
is
relative
risk.
For
this
example,
we
choose
relative
risk.
Again,
bring
this
equivalent
test
launch
dialog.
For
this
example,
we're
going
to
run
noninferiority
test.
A
gain,
we
prefer
large
ratios.
We
specify
0.9
as
our
margin
and
we
care
about
the
treatment
effect.
So
we
should
choose
Yes
and
then
we
click
OK.
So
this
bring
the
noninferiority
test.
From
the
forest
plot,
we
can
see
the
confidence
interval
of
the
relative
risk
is
completely
contained
in
this
blue
region,
and
the
P
value
for
the
noninferiority
test
is
very
small.
So
we
conclude
at
the
5
%
significant
level,
the
drug
FIDAX
is
not
inferior
to
drug
VENCO.
This
concludes
my
talk
and
I
will
give
back
to
Mark.
I
need
to
stop this
share.
Thank
you,
Jianfeng.
Now
in
the
last
part
of
our
presentation,
I'm
going
to
talk
about
another
comparison
where
we
want
to
compare
the
results
or
measurements
from
two
different
methods
of
measuring
some
quantity.
We
assume
that
there's
a
standard
method
that
already
exists.
It
has
been
validated.
We
can
use
it
to
measure
the
level
of
some
quantity.
That
might
be
the
temperature
or
the
potency
of
a
drug.
But
for
some
reason,
we've
developed
a
new
method
for
the
same
result.
We
must
compare
its
performance
to
the
standard
method
before
we
use
it.
This
is
a
long
standing
issue.
This
comparison
has
been
codified
for
a
long
time
by
numerous
international
organizations.
I've
listed
a
few
of
them
on
this
slide.
So
this
is
a
very
well
studied
and
an
established
comparison.
In
this
case,
we're
going
to
compare
to
identity.
We're
going
to
compare
these
two
methods
where
ideally
the
test
method
would
give
us
the
same
value
as
the
standard
method.
So we
plot
the
data
using
a
scatter plot.
We
have
the
test
method
on
the
vertical
axis
and
the
standard
method
result
on
the
horizontal
axis.
We
can
even
plot
the
identity
line
where
Y
equals
X
for
reference.
Ideally,
we
would
get
the
result
from
both
methods,
but
that
won't
happen
because
of
measurement
error
in
both
methods.
We'll
use
regression
analysis
to
determine
the
best
fit
for
this
line
where
we
have
the
test
method
versus
the
standard
method.
Then
the
estimated
parameters
for
our
model
can
be
compared
to
the
identity
line.
The
null
hypothesis,
we
start
with
the
idea
that
they
are
not
the
same.
So
the
intercept
of
this
line
is
not
zero
or
the
slope
is
not
one
or
possibly
both.
In
other
words,
the
results
are
not
equivalent.
The
alternative,
which
we
would
like
to
claim
is
that
they
are
equivalent.
So
there
we
state
that
the
intercept
should
be
zero
and
the
slope
should
be
one.
To
make
this
comparison
using
regression,
we
have
to
postulate
a
model.
In
this
case,
it's
a
simple
linear
regression
model.
We
have
a
constant
term
A,
a
proportional
term
B
times
X.
We're
going
to
estimate
those
parameters
A
and
B, and
use
our
hypotheses
to
decide.
We
also
have
a
term
epsilon,
and
that
represents
the
measurement
error,
the
random
variation.
Using
linear
regression,
we
assume
that
the
Y
and
the
X
are
linearly
related.
We
assume
that
the
statistical
errors
that
epsilon
are
in
Y,
not
in
X.
We
also
assume
that
those
errors
are
distributed
in
a
way
that
are
independent
of
the
response.
In
other
words,
the
statistical
error
is
the
same
across
the
entire
range
of
this
method.
Also,
that
no
data
exert
any
excessive
influence
on
these
estimates.
Well,
in
method
comparison,
we
usually
violate
these
assumptions.
First
of
all,
there
is
measurement
error
in
the
standard
method
as
well.
Also,
often
the
errors
are
not
constant.
That
is,
we
observe
a
constant
coefficient
of
variation
but
not
a
constant
standard
deviation.
Outliers
are
present that
can
strongly
influence
the
estimation.
Other
regression
methods
are
required
in
such
a
case.
The
Deming
regression
simultaneously
minimizes
the
least
square
error
in
both
Y
and
X.
That's
appropriate
for
this
case.
The
Passing-
Bablock
regression
is
a
nonparametric
method
that's
based
on
the
median
of
all
possible
pairwise
slopes.
It's
resistant
to
outliers
and
nonconstant
errors.
Let's
talk
about
each
of
these
briefly.
The
Deming
regression
is
provided
in
the
Bivariate
platform
through
the
Fit
Orthogonal
Command.
This
has
been
available
in
JMP
for
many
years.
The
Deming
regression
can
estimate
the
errors
in
Y
and
X,
assume
that
the
errors
in
Y
and
X
are
equal,
or
use
a
given
ratio
of
Y
to
X
error.
Passing-
Bablock
regression
is
new.
JMP® 17
introduced
this
method
in
the
Bivariate
platform
through
the
Fit
Passing-Bablock
command.
This
command
also
includes
checks
for
the
assumptions
that
the
measurements
are
highly
positively
correlated
and
exhibit
a
linear
relationship.
Method
comparison
often
includes
a
comparison
by
difference.
The
Bland-
Altman
analysis
compares
the
pairwise
differences
as
Y
to
the
pairwise
means
as
X
to
assess
bias
between
the
two
values.
The
results
are
presented
in
a
scatterplot
of
Y
versus
X
for
your
examination
and
also
to
identify
any
anomalies.
This
occurs
in
the
Matched
Pairs
platform. The
Matched
Pairs
platform
has
been
part
of
JMP
for
many
years
as
well,
but
the
Bland-Altman
test
is
a
new
addition.
The
report
also
presents
the
hypothesis
test.
Now
I'd
like
to
demonstrate
these
two
methods.
As
I
said,
Deming
regression
has
been
available
for
a
long
time,
but
for
completeness'
sake,
I'm
going
to
demonstrate
it
here
alongside
the
new
methods.
I
select
from
the
Analyze
menu,
Fit
Y
by
X.
I'm
going
to
compare
test
Method 1
to
my
standard
method.
The
standard
method
goes
in
the
X
role
and
the
new
test
method
goes
in
the
Y
role.
You
could
evaluate
more
than
one
test
method
at
the
same
time.
Here's
my
plot.
Initially,
I
see
a
scatter
plot.
I
expect
these
two
to
agree
very
well.
I
expect
to
see
that
they
follow
this
diagonal
path,
that
they're
linear,
and
so
forth.
I'll
click
the
red
triangle
next
to
Bivariate
and
select
Fit
Orthogonal.
In
this
case,
I
don't
really
know
that
the
variances
are
equal
or
I
don't
have
any
prior
information
that
I
could
specify
a
ratio.
So I'll
have
JMP
estimate
the
errors
in
both.
I'll
use
the
first
option
here.
Now
we
have
the
fitted
line
using
Deming
regression.
Below
that
we
have
the
report.
The
report
includes
an
estimate
of
the
intercept.
We
can
see
it's
small
and
close
to
zero.
We
have
an
estimate
for
the
slope.
Using
the
confidence
interval,
we
see
that
it
includes
one,
which
we
would
expect
if
the
test
method
agrees
with
the
standard.
That's
Deming
regression.
Now,
we're
going
to
take
a
look
at
the
new
methods
Passing-Bablock
regression
in
Blad-
Altman.
Same
start,
select
Analyze,
and
then
select
Fit
Y
by
X.
I'm
going
to
use
the
Recall
button
here.
I
want
to
compare
Method 1
to
the
standard,
but
I'm
going
to
use
a
new
regression
technique.
I'll
click
on
the
red
triangle
and
select
Fit
Passing-Bablock.
There's
actually
two
lines
here.
There's
a
red
line
that
represents
the
best-
fit
line
using
the
Passing-Bablock
regression.
But
there's
also,
for
our
reference,
a
line
that
represents
where
y
equals
x.
It's
hard
to
see.
I'm
going
to
use
the
magnifier
tool
to
magnify
a
few
times.
Now
you
can
see
that
there
are,
in
fact,
two
separate
lines.
One
is
the
identity
and
one
is
the
fit,
but
they
overlap
quite
a
bit.
These
are
quite
similar.
In
the
numerical
reports,
first
I
have
a
test
for
the
high
positive
correlation.
We're
using
Kendall's
Tau,
and
we
can
see
that
it
is
highly
significant.
We
reject
the
idea
that
they're
not
strongly
correlated.
Next,
we
have
a
test
of
linearity.
Here,
this
test
assumes
that
they're
linear, and w
e're
looking
for
strong
evidence
against
that.
But
we
have
a
very
high
P
value
here,
so
we
do
not
reject
the
assumption
that
they're
linear.
Finally,
we
have
the
parameter
estimates
by
using
Passing
-Bablock
regression,
we
have
the
point
estimate,
we
have
the
interval
estimate.
For
the
intercept,
that
interval
includes
zero,
so
we
cannot
reject
an
intercept
of
zero.
We have
the
slope
is
contained
within
an
interval
that
includes
one.
Similarly,
we
can't
reject
that
the
slope
of
that
line
is
equal
to
one.
Let's
say
we'd
also
like
to
compare
these
two
methods
by
difference.
To
do
that,
I
click
on
the
red
triangle
for
the
options
of
the
Fit
Passing-Bablock
results, and
here
we
see
the
command
Bland-Altman
analysis.
It
takes
all
the
information
here
and
launches
match
pairs
with
the
additional
information
in
the
Bland-Altman
analysis.
The
plot
is
showing
us
on
the
Y
axis
the
pairwise
difference
between
the
method
one
and
the
standard
method,
and
it's
plotted
against
on
the
horizontal
axis
is
the
mean
of
those
two
values.
The
Bland-Altman
analysis
is
helpful
because
it
gives
us
an
idea
about
the
bias.
So
here
I
have
an
estimate
of
the
bias
is
negative
0.113,
but
we
can
see
that
the
interval
estimate
of
the
bias
includes
zero,
so
we
can't
reject
the
idea
that
the
bias
is
equal
to
zero
and
so
on.
Now
we
have
in
JMP® 17,
a
much
more
complete
set
of
tools
for
comparing
different
test
methods.
That
concludes
our
presentation.
Thank
you.
