Hello.
I
am
Jose
Carvalho,
a
statistician
at
Statistic
Consulting
in
Campinos,
Brazil.
I
thank
you
for
the
opportunity
to
show
an
application
of
JMP
to
clinical
trials
where
a
major
improvement
movement
came
from
a
statistical
discovery.
As
a
result
of
that
discovery,
one
trial
ended
with
the
expected
and
very
desired
results.
Subsequent
trials
on
the
same
syndrome
will
be
much
cheaper
and
faster.
The
problem
is
the
bleeding
after
birth
or
postpartum
haemorrhage,
P
PH
for
short.
P
PH
accounts
for
125,000
deaths
per
year.
Even
in
developed
countries
like
the
United
States,
it's
the
cause
of
11%
of
the
maternal
deaths.
Now,
PPH
is
defined,
just
for
classification,
as
blood
volume
in
excess
of
500 mL
in
24
hours
after
delivery.
If
the
volume
exceeds
1,000
mL,
then
it's
severe
PPH.
It'll be
interesting
to
know
the
main
cause
of
PPH.
90%
of
the
cause
are
uterus atony
.
It's
a
failure
of
the
uterus
to
contract
after
the
delivery.
If
the
uterus
failed
to
contract,
then
the
bleeding
continues.
Then
we
can
treat
that
by
giving
drugs
to
contract
the
uterus
or
by
some
physical
action.
The
main
cause
are
trauma
and
placental tissue
retention
and
coagulation
system
failure.
You'll
be
dealing
with
uterus atomy
and
its
prevention.
PPH
can
bring
serious
threat
to woman's
life
and
health.
Its
onset
must
be
quickly
diagnosed
during
the
delivery
and
treated.
Treatments
include,
as
I
said,
drug
treatment
with
additional
uterotonics
and as a last
resort,
artery
irrigation
or h
ysterectomy,
the
uterus
removal.
New
drugs
and
devices
are
being
developed
to
prevent
PPH.
Every
one
of
those
must
be
tested
in
clinical
trials
before
they
are
allowed
to
use
in
the
natural
deliveries.
We
have
data
on
three
very
large
trials.
The
first
one,
the
oldest
one,
was
published
in
2001.
It
was
the
Misoprostol.
That's
the
name
of
a
drug
that
was
compared
to
the
standard
treatment
and
used
18,000
women.
The
second
one
after
that,
and
that
was
published
in
2012,
is
the
Active
Management,
not
a
drug,
but
a
physical
procedure
of
pulling
the
umbilical
cords.
Now,
the
Misoprostol
didn't
prove
to
be
as
effective
as
the
standard
drug
of
treatment,
which
is
oxytocin.
The
Active
Management
did
not
show
any
improvement
also
on
PPH.
Now
we're
going
to
deal...
Sorry,
with
the
Carbetocin trial
,
published
in
2018,
the
largest
of
all,
that
enrolled
29,000
women.
In
all
these
trials,
the
primary
outcomes
were
severe
PPH
and/or PP
H.
Now,
to
diagnose
sPPH
and/or PP
H,
we
need
to
know
the
blood
volume.
The
observations
were
volume,
the
numbers
volumes
in m
L.
But
only
the
indicators
of
SPPH
and
PPH
were
considered
in
the
statistical
analyses.
That
is
binomial
variance zero,
one,
yes
or
no.
Okay,
in
spite
that
we
had
the
information
about
the
blood
volume.
Before
we
proceed,
just
a
small
explanation
about
the
two
drugs
that
we'll
be
dealing
with, again.
The
standard
drug
to
use in
deliveries
is
oxytocin.
It's
given
routinely
to
every
delivery
work,
every
part
in
the
world.
As
soon
as
the
baby
is
delivered,
the
woman
receives
a
shot
of
oxytocin.
It's
a
standard
procedure.
Now,
oxytocin
is
very
nice.
It
reduces
the
severe
PPH
rates
from
3.84%
to
2%.
It
helps
the
incidents
or
the
rates
of
the
sPPH.
But
there
is
a
problem,
it
is
a
heat-labile
substance.
It
must
be
kept
in
a
cold
chain
at
seven
centi
grades
all
the
time.
Now,
in
countries
with
low
resources,
this
can
be
a
problem.
If
you
do
not
keep
it
in
this
cold
chain
logistic,
the
drug
will
lose
its
efficacy,
I'm
sorry.
Sometimes
you
can
apply
a
drug
that
is
not
effective
at
all.
Now,
carbetocin,
it's
a
new
drug
which
has
the
same
active
principle
of
oxytocin
and
just
a
change
in
the
excipients
that
makes
it
heat-
stable.
Carbetocin
can
be
kept
for
six
months
at
30
centigrades,
which
is
about
room
temperature
in
most
places
in
the
world.
Now,
there
were
very
high
hopes
that
carbetocin
would
be
a
good
replacement
for
oxytocin,
most
of
all
for
use
in
those
low-
resource
countries.
A clinical
trial
was
devised
for
PPH,
it
was
done
by
the WHO
and
it
was a
non-
inferiority
trial.
The
parameters
for
this
trial
are
in
the
objective.
The
investigators
said
that,
to
declare
carbetocin
non-
inferior
to
oxytocin,
it
should
preserve
75%
of
the
benefits.
Now,
the
benefit
is
this
3.84%
minus 2%
so
this
gives
them
non-inferiority
margin
of
0.4
6%.
We
are
talking
about
very
low
rates
and
the
relative
risk
of
1.23.
Carbetocin
would
be
declared
non-inferior
to
oxytocin
if
in
the
trial
we
could
prove
or
bring
evidence
on
that
a
relative
risk
is
less
than
1.23.
This
result
in
just
a
mazing
competition
in
a
sample
size
with
over
30,000
people.
We
ended
up
with
a
trial
with
about
29,000.
Those
were
in
several
countries
as
we
signed
that
table
before
in
the
many
centres.
It
was
a
very
expensive
trial,
just
a
data
collection
of
well
over
almost
two
years.
It's
a
very
serious
thing.
Why
are
the
trials
so
large?
Well,
the
obvious
response
answers
to
that
question,
is
the
proportion
being
compared
are
small.
The
effects
are
necessarily
even
smaller.
Not
so
obvious,
but
it's
still
obvious,
that
the
triumph
needs
to
be
so
large
because
we
are
losing
a
great
deal
of
information
by
mapping
V,
the
volume,
into
two
categories,
like
this.
On
this
histogram
here
we
have
the
actual
distribution
of
the
blood
loss
volume
for
the
29,000
subjects
of
the
trial
and
then
the
cut-
off point, thousands
.
Just
imagine,
just
looking
at
the
histogram,
how
much
information
is
lost
by
taking
all
the
niceties
of
the
frequency
of
the
histogram
in
zero, one
left
to
the
thousand
line,
right
to
the
thousand
line.
But
that's
the
way
it
was
done,
because
for
some
reasons
people
like
to
use
this
dichotomization.
If
it's
over
1,000,
it's
severe
PPH.
If
not,
it's
not.
I
don't
know
even
if
that's
well
too
associated
with
any
further
consequence
on
the
health
of
the
women.
That's
the
way
it's
done.
The
classification
is
that.
Now
JMP
helped
us
to
discover
that
the
distribution
of
the
blood
loss
volume
is
log
normal.
There
is
a
story
behind
it.
We
set
forth
to
analyze
the
experiment
as
decided
by
the
investigators
used in
the
binomial
distribution.
But
we
saw
that
very
easily
that
the
two
distributions
of
carbetocin
and
oxytocin,
the
blood
loss volume
distribution
were
pretty
much
the
same.
We
were
not
very
happy
with
this dichotomization
to
begin
with,
but
we
had
to
do
it.
That's
what
the
protocol
said.
Now,
once
we
the
statisticians
at
the
trial,
we
found
beyond
any
doubt
that
the
distribution
was
log
normal.
When
I
say
the
distribution
of
blood
loss
volume
is
log
normal,
I
mean
a
big
if, it is,
it is
not
an
approximation,
a
nice
fit,
things
that
we
statisticians
like.
No,
we
had
29,000
points
and
the
fit
you
are
going
to
see
was
perfect.
Then
we
went
to
do
some
homework
and
we
found
from
physics
that
the
blood
loss
volume
distribution...
Excuse
me,
the
fluid
volume
in
pipes
has
a
log
normal
distribution,
and
that
has
been
known
since
the
19th
century.
Coming
from
physics.
Of
course,
we
realized
that
our
pipes
are
blood
vessels,
so
they
are
elastic.
The
viscosity
of
the
blood
changes
because
of
coagulation.
But
still,
we
have
sort
of
a
model.
We
have
fluid
in
pipes,
flowing
in
pipes,
and
the
data
showed
that.
We
were
very
excited
with
that.
We
went
further
to
see
the
consequence
of
using
V
for
the
estimation
of
the
risk
and
we
got
nice
results.
Now
then
we
had
to
convince
the
investigators.
Such
a
large
trial
has
lots
of
investigators,
big
shots.
The
physicians,
they
own
the
problem,
so
they
have
the
last
word
and
everything.
They
thrown
at
the
idea.
Some
of
them
really
didn't
like.
They
said,
"Well,
we
use
no
hypothesis
since
it
just
binomial
variant,
it
has
no
model."
It
has,
but
they
think
it
doesn't.
People
think
it's
too
simple.
What
if
the
log
normal
distribution
is
not
correct?
We
can
have
wrong
results.
Then
we
did
exactly
what
we're
going
to
do
right
here.
Now,
we
did
the
analysis
in
front
of
them,
and
that
with
JMP
was
very
compelling,
and
I
hope
you
agree
with
that.
JMP
helped
also
on
the
communication
of
the
discovery
to
the
investigators
in
a
very
compelling
way.
Just
then,
to
advance
the
result,
using
the lognormal
distribution
saves
the
results
of
the
experiment.
That's
part
of
the
story.
We
went
on
then
to
publish
those
results
after
the
publication
of
the
experiment
was
done,
because
the
experiment
failed.
You'll
see
that
it's
a
nice
story.
But
then
we
published
the
results
with
the lognormal
distribution
as
a
secondary
analysis.
That
touched
the
hearts
of
the
European
authorities,
like the ADMA.
Right
now,
carbetocin
is
very
happily
being
used
in
low-
resource
countries,
where
it
is
needed.
We are
very
happy
with
that.
Let
me
show
you
how
it
went.
First
of
all,
the
measurement.
You
see
on
the
left,
a
sort
of
collector
to
collect
the
blood.
It's
used
in
many
case
in
deliveries.
As
I
told
you,
sometimes
you
have
to
take
very
fast
action
when
the
woman
is
bleeding
too
much.
People
can
evaluate
the
blood
loss
by
just
seeing
the
stain
in
the
bed,
in
the
floor.
But
in
many
case,
people
use
that
collector.
That
collector
has
a
scale
that
I
enlarged
on
the
right.
In
the
first
two
trials,
the
blood
loss
volume
was
evaluated
with
that.
Then
they
changed.
They
changed
because
it
was
no
good,
not
perfect
for
our
experiments,
the
three
of
them
that's
been
running
for
about
20
years
now.
Let
me
show
you
how
it
goes
with
JMP.
Let's
see.
I
feel
more
comfortable
with
JMP.
Here
is
a
data
table
with
all
the
71,000
case
of
the
three
trials.
Miso
prostol.
Here
they
are.
Mis
oprostol, A
ctive
Management,
and
Carbetocin
.
Let's
see
the
distribution
of
the
blood
loss
volume
for
three
of
them
by
trial,
not
by
treatment.
The
difference
by
treatment
is
so
small
that
it
won't
matter
for
this
short
demonstration
here.
I'm
not
analyzing
the
experiment
yet.
Here
is
for
Misoprostol
distribution.
You
see
that
it's
a
very
nice
log
normal,
isn't
it?
Can
be
something
else,
but
it
is
log
normal.
It
looks
like
a
nice
distribution,
but
it
has
problems.
It's
hiding
the
problems,
actually,
not
for
fitting
a
log
normal,
but
for
analyzing
the
way
it
was
with
the
binomial
variants.
Let's
use
the
Grabber
tool
and
change
the
pins
of
the
histogram.
Make
them
thinner.
Okay,
there
we
go.
What
we
see,
we
see
spikes
in
distribution.
Regularly
you
have
spikes,
you
can
see
them
here.
Let
me
change
a
little
bit, yes.
Now
you
said,
well,
there's
no
problem.
It's
like
numerical
integration.
You
lose
on
the
one
beam
then
you
have
access
on
the
other
beam
and
they
alternate
and
you
end
up
with
a
nice
integration.
Well,
not
the
case
here,
because
we
have
a
problem
that
in
1,000
we
have
a
cut-
off
here.
Let
me
take
a
zoom
of
distribution
around
1,000,
which
matter
most
for
us.
See
here's
the
spike
at
1,000
.
But
you
see
part
of
this
frequency
here
comes
from
the
left,
from
the
900.
Because
of
the
reading
of
that
scale,
that
scale
was
rough
and
people
tended
to
round
the
numbers.
There
is
a
sort
of
a
digit
preference
here.
It's
very
clear
that
some
of
the
known
cases
of
PPH
were
moved
to
PPH.
It's
no
trivial
quantity
for
that
small
frequency
here.
That
means
that
in
spite
of
having
no
model,
as
my
colleague
said,
for
the
binomial
variants,
we
probably
have
a
positive
bias
on
this
estimation.
Now,
this
problem
was
taken
care
of
by
taking
the
weight
of
the
collector
device
before
the
collection,
before
the
use,
before
the
procedure,
and
then
weighing it
again
after
the
procedure.
That
was
done
only
for
the
carbetocin
trial
that
started
on
the
carbetocin
trial.
If
you
go
the
same
trick
here,
change
the
beams.
Now
you
see
that
we
have
a
nice
distribution,
no
problem
with
spikes
anymore.
Weighing
solved
that
problem.
Now,
let
me
tell
you
this
collector
is
not
for
the
experiment.
It's
for
actual
clinical
use.
The
evaluation
of
the
blood
loss
and
its
speed
during
the
delivery
is
perfect
with
that
scale.
We
cannot
remove
that
and then
weigh
then
to
decide
that,
you
have
to
take,
say,
a hysterectomy or thing
like
that.
It's
still
in
place,
it's
still
used
like
that.
We
just
changed
it
for
the
trial.
We
wait
at
the
end.
That's
just
for
curiosity
or
something
interesting.
That
came
also
from
the
ability
that
we
have
so
easily
to
do
this
sort
of
analysis
with
JMP.
That's
more
important
than
we
can
even
think
of.
Now
let's
go
to
the
real
problem.
It
is
also
easy
with
JMP.
I'm
going
to
analyze
the
results
of
the
carbetocin
trial
but
then,
so
that
I
don't
get
mixed
up
in
front
of
you,
I
prepared
data
set
with
subset
just
for
the
carbetocin trial
.
Here
it
is,
29,000 case
only.
That's
a
subset
of
that
other
trial.
Let
me
take
the
opportunity
to
tell
you
what
the
data
that
I
have
here,
of
course,
that's
not
the
full
data
of
the
trial,
that
clinical
trials.
Clinical
trials,
you
collect
the
hundreds
of
columns
of
[inaudible 00:20:56]
for
many
reasons
and
for
controlling so on
and
so
forth.
Here
we
have
just
the
center,
because
the
experiment
was
randomized
by
center
so
I
have
to
keep
it.
Then
the
arm,
it's
one
and
two
here
but
here
have
the
issue
that's
closed
and
I
have
open
treatment
and
control
here
the
trial is
over,
of
course.
Then
the
volume,
that's
all
the
data
we
need.
Those
two
columns
here
are
derivations,
are the sPPH indicator and
P
PH
indicator
so
they
are
just
very
easy
to
do.
Just
an
indicator
of
[inaudible 00:21:48]
PPH in
this
case.
Let's
start
by
analyzing
the
way
protocol
SEBs, perhaps
in
a
simple
way,
not
doing
the
complete
analysis,
but
let's
analyze
the
SPPH
response.
Remember...
Not
remember,
I
didn't
say
that
yet.
In
the
actual
trial
analysis,
we
came
to
the
relative
risk
of
1.26
and
the
maximum,
I
told
you,
for
non-inferiority
was
1.23.
So it
was
a
near
missed
situation.
We
could
not
declare
non-inferiority
and
if
you
go
to
the
publication
of
the
experiment,
you
can
find
in
the
reference
in
the
last
slide
here.
We
have
to
publish
that
we
didn't
prove
non-inferiority,
much
to
our
regrets.
Let's
go
and
do
it
just
to
show
that's
a
sort
of
show
off
for
JMP.
How
we
need
now
is
a
fit
Y
by
X.
It's
so
simple
after
all
that
work.
We
have
treatment
for
X
and
we
have
to
use
block
for
centers,
just
to
respect
randomization.
And
there
we
can
explore
the
results
of
this
here.
But
I'm
looking
just
for
the
relative
risk,
which
is
one
item in the...
It's
one
item
on the
menu
here,
relative
risk.
Well,
one
is
our
response
and
treatment
must
be
in
the
numerator.
That's
our
choice.
There
we
go.
We
have
down
here
1.255,
that's
the
1.26
that
we
got
with
those
nice
models,
random
models
for
center
and
things
like
that.
So
it's
1.25.
It's
a
near
miss
situation.
We
didn't
prove
non-inferiority.
Instead
of
just
weeping
over
the
results,
we
went
on
and
tried
to
do
an
analysis
that
was
not
planned,
but
anyway,
we
published
it
as
a
sort
of
secondary
analysis
afterwards.
Let's
analyze
the
distribution
of
v.
To
do
that,
I'm
not
going
to
the
distribution
platform.
Rather,
I'm
going
to
use
reliability
and
survival,
life distribution
because
it's
a
much
richer
platform
for
studying
distributions,
except
that
the
variable,
the
column
must
be
non-
negative.
That's
the
case
for
volume,
okay.
I
can
use
this
instead
of
timing
here.
I
don't
need
sensory,
nothing
like
that.
There's
no
such
a
thing
here.
It's
just
a
tool
for
fitting
distributions.
Now
let's
get
down
to
business here.
I
have
distribution
of
both
treatment
and
control,
that
is
carbetocin
and
oxytocin.
Let's
separate
those.
You
can
do
that
by
a
local
data
filter
for
treatment
and
then
I'll
choose
treatment
here,
that's
carbetocin.
On
the
right
here
we
have
the
data
points,
those
black
dots,.
They
are
so
many,
15,000
of
them.
Those
that
were
treated
with
carbetocin,
that
they
look
like
a
continuous
line
but
those
are
the
points.
They're
not
having
blue,
they're
nonparametric
estimates,
nonparametric estimates
[inaudible 00:25:41].
They
are
the
same
as
the
binomial
point
wise,
because
they
have
no
sensory.
Then
where's
the
lognormal
here?
There's
no
lognormal
in
the
menu
for
distributions.
That's
because
there
are
zeros
in
the
data.
Then
we
cannot
fit
a
lognormal
with
two
parameters.
Some
women
are
very
lucky
enough
to
have
[inaudible 00:26:06] zero
millilitre for
blood
loss.
Probably
that
was
some
mistake.
There
were
women
that
went
almost
to
4000
in
the
control
and
those
were
probably
in
shock.
This
large
span
here
for
the
binomial
variation
was
separating
just
two.
Okay,
let's
fit
the
threshold
lognormal.
The
lognormal
that
you
take
a
shift
so
that
we
can
put
the
zeros
in
the
field.
Now
we
have
three
lines
here.
The
red
one
is
the
threshold
lognormal.
They
are
all
three.
They
are
hiding
themselves,
the
three
of
them.
Then
people
can
say
"Well,
okay,
the
fit
is
very
nice,
perfect."
It's
not
always
like
that.
If
I
fit
a
normal
or
a
smallest
value
here
things
like
that ,
you
can
see
that
you
come
out
but
that's
no
need here.
We
can
find
the
risk
in
several
place
in
this
result here.
The
risk
is
one minus 0.985.
If
you
don't
want
to
do
this
sort
of
subtraction,
we
can
show
the
survival
curve
and
the
risk
is
1.47
for
carbetocin.
If
I
want
to
see
the
risk
at
1,000
for
oxytocin,
it's
again
the
same,
1.47.
Wow.
We
have
also
confidence
interval
here.
People
will
challenge
us
say,
"T
hose
distributions,
they
look
the
same
because
of
the
scale
of
the
graph."
Well,
let's
take
up
this
challenge.
Let's
do
a
zoom
here.
Let's
do
a
zoom
around
1,000.
Just
because
we
are
caring
about
that.
Look
how
close
the
fit
is.
It's
very
close.
Now
I
can
go
even
further,
like
this.
And
now
we
can
see
even
more.
We
see
that
the
point
estimates
this
black
dot
here,
if
you
want,
it's
almost
the
same
as
the
red
line,
which
is
the
lognormal
fence.
My
fellow
investigators
there
could
see
that
I
don't
have
expressions
or
a
table.
A
table
won't
say
anything.
They
could
evenâ
I
don't
knowâ
but
they
could
even
think
that
statisticians
were
cheating.
Here
is
the
easy
way
to
show
it
but
there
is
more
to
see
here.
If
you
see
the
confidence
interval
for
the
lognormal
distribution,
it's
one
third
of
that
of
the
nonparametric distribution.
Well,
since
the
precision
goes with the...
increase with
the
square
root
of
the
experiment
size.
We
can
guess
that
if
we
take
size
one
ninth
of
that
I
would
get
for
the
lognormal
the
same
confidence
interval
that
I
get
for
the
nonparametric
here.
That's
interesting.
Instead
of
using
30,000
women,
essentially
I
could
use
3000
and
get
this
result.
That
was
very
good
for
the
investigators,
they
planned
on
that.
This reduced
[inaudible 00:29:55]
of
the
confidence
interval
came
from
the
lognormal,
which
was
not
planned.
So
something
else
to
hear.
Well,
okay,
you're
doing
fine
for
the
risk
.
You're
getting
the
risk
from
the
log
normal
which
is
the
same
as
the
binomial
rate
and
you
have a
closer
confidence
interval
if
the
log
normal
assumption
is
okay,
it
is.
Now
what
about
the
relative
risk
?
Well,
we
can
go
and
take
the
logarithm
of
the
V.
You
have
a
normal
distribution
so
we
have
a
standard
apparatus
to
do
some
regressions
and
find
the
relative
risk.
But
I
remember
John
Sol
talking
on
this
same
meeting
last
year.
His
talk
has
a
nice
title,
Delicate
Brute
Force.
Let's use
the
same
thing,
delicate
brute
force.
If it's
good
for
John Sol,
it's
going
to
be
good
for
us
too.
Here
is
the
estimation...
the estimated
parameters
of
the
lognormal
that
we
get.
If
we
can
do
a
bootstrap
sample
of
this,
we
can
compute
the
risk,
the
bootstrap
risk.
We
have
a
bootstrap
sample
for
the
risk.
We
can
do
that
for
carbetocin
and
for
oxytocin
and
that's
good.
Then
you
say,
"Well,
I
have
to
program
this."
"I
have
to
program
the
bootstrap
sample."
It's
not
difficult
but
you
have
to
program
and
then
you
have
to
compute
1000
times,
2000
times,
whatever it is
lognormal
fits.
But
no,
JMP
is
nice
twice.
If
you
click
with
the
right
button,
this
table,
you
have
bootstrap
on
the
menu.
The
suggestion
is
to
take
2500,
we
can
take
5,000
or
whatever,
but
it
takes
a
long
time.
We
did
that
with
1,000.
We
were
very
happy
with
that.
It
takes
10
minutes
or
so
for
each
of
treatment
and
carbetocin
or
oxytocin.
I'm
not
going
to
make
you
wait
10
minutes,
I
didn't
want
to
wait
for
longer, right.
We
did
that
before
and
here
is
the
bootstrap
sample
for
the
control.
I
mean,
that
is
oxytocin.
The
output
are
the
parameters
here.
The
first
line
is
the
actual
result
of
the
experiments
and
all
the
rest
is
1,000
bootstrap
samples
that's
why
we
have
1,001
here.
Now
this
column
here
came
from
the
parameters.
It's
just
the
risk
estimate.
One
minus
the
log
normal
distribution
at
the
point
1,000 minus
threshold,
location
and
scale.
Fine,
easy.
Now
here's
the
same
thing
for
carbetocin.
Now
I
use
a
result
that
I've
read
the
book
by
[inaudible 00:33:18]
,
the
man
who
knows
everything
about
bootstrap.
To
have
a
bootstrap
sample
of
the
relative
risk,
all
I
have
to
do
is
take
those
two
bootstrap
samples
here
and
join
the
tables
row
wise.
It's
a
Mickey
Mouse
operation
for
JMP,
like
we
do
with
the
tables
here
and
so
on.
Here's
the
results.
I
kept
just
the
risks
column
here
for
carbetocin
here
and
oxytocin.
If
you
don't
want
to
use
this
extra
point,
I
don't
know
why
you
wouldn't
bu
t.
We
can
exclude
it
to
use
just
the
bootstrap.
We
have
the
relative
risk
here,
just
the
quotient
of
those
two
columns.
We're
done.
Take
the
distribution
of
this
bootstrap
sample,
the
relative
risks
and
here
we
are,
we're
almost
to
celebrate
now.
Here's
the
distribution.
You
don't
see
1.23
here...
Yes,
you
see,
but
then
we
need
now
one
sided
confidence
interval
with
95%
coverage.
I
need
the
5%
quantile,
which
is
not
here.
Okay,
so
we
kindly
asked
JMP
to
compute
that
you
can
put
display
options,
custom
quantiles
and
we
need
0.
95 quantile,
which
turns
out
to
be
1.11.
We
even
have
a
bonus
result
which
is
the
confidence
interval
for
this
estimate.
If
you
want
to
be
really
safe,
we
can
use
the upper,
the upper
confidence
limit...
For
the
limit of the confidence limit
that's
too
involved
to
say.
Anyway,
it's
far
away
than
1.23.
Then
we
have
proven
in
some
sense,
we
have
thrown
evidence
that
carbetocin
is
non-
inferior
to
oxytocin.
That's
the
result
we
published.
A
s
I
told
you,
that
publication
with
some
work
by
the
investigators,
it's
warmed
the
hearts
of
the
EMA,
the
European
authority
who
was
overlooking
this
trial
and
carbetocin
is
not
being
used
on
places
where
you
have
no
code
chain
assured.
Let
me
use
your
time
if
I
can,
just
to
show
the
efficiency
that
we
get.
Let
me
go
back
to
presentation
here.
Let's
see
the
relative
efficiency
of
binomial
versus
lognormal.
Let's
take
the
problem,
not
non-inferiority
but
simple
problem
of
testing the
superiority
of
a
new
drug
over
oxytocin.
The
new
drug
would
be
declared
superior
if
it's
risk
for
sPPH
is
less
than
1.5%
compared
with
2%
of
oxytocin.
We
have
here
all
we
need
to
do
a
binomial
test.
For
the
lognormal
test
we
need
to
convert
from
this
piece
to
the
means.
Let's
do
it.
For
the
[inaudible 00:37:15]
,
you
have
this,
for
the
lognormal,
we
just
do
this.
We
want
to
know
the
risk,
is
the
probability
of
being
larger
than
1,000 so we
take
logs
on
both
sides,
no
subtract
then
standardized,
which
is
now
a
normal
variance.
And
here
for
S,
the
standard
deviation,
use
0.7.
It's
important
in
every
bit
that
we
did,
every
inference
that
we
did
with
those
three
trials
and
then
a
few
more,
that
we
have
data
available.
Every
time
the
standard
deviation
came
about
0.7.
We
called
jokingly,
this
is
a
universal
constant
of
P
PH.
The
standard
deviation
of
the
logarithm
of
the
blood
volume
loss
is 0.7
we
replace
S
by
0.7.
We
compute
the
quantiles
at
2%
and
then
1.5% here
and we
solve
the
equations
on
them
and
we
have
those
two
means
to
compare
with
a
normal
distribution.
The
difference
is
0.08
14.
Okay,
now
we
go
back
to
JMP.
I
feel
even
ashamed
of
showing
that
but
it's
fun
and
we
did
that
for
our
medical
team
there
and
it
was
very
compelling,
as
I
told
you.
Sorry
about
all
those
windows
open
here.
Very
simple.
You
come
to
DOE,
Sample
Size
Explorer,
Power,
it's mickey
mouse
stuff.
Let's
do
that
for
the
sample
size
for
two
independent
sample
proportions.
We
have
one
sided,
it's
a
superiority
test.
The
proportion
under
the
new
is
2%,
under
the
alternative
is
1.5%.
It's
going
to
change
to
too
much,
1.5
in
hiding
here.
Then
we
want
8%
for
Power
and
the
sample
size
is
17,000.
Okay,
17,000
to
go
old
fashioned.
Let's
compute
the
sample
size
for
the
lognormal.
Using
Sample
Size
Explorers
from
DOE,
Power
and
Power
for
Two
Independent
Sample
Means.
We
have
a
one
sided
test.
We
have
to
add
the
standard
deviations
which
are
0.7
for
both
groups
and
then
the
difference
to
detect
we
had
compute
0.0814
and
we
want
80%
Power.
Okay,
we
came
to
the
result.
Now,
the
sample
size
or
the
experiment
size
is
1831.
That's
about
one
ninth
of
the
17000
that
we
had
computed
for
the
binomial
[inaudible 00:40:31].
That's
what
we
got
by
just
inspecting
the
the width
of
the
confidence
intervals
in
the
reliability
platform.
That's
how
much
more
efficient
using
lognormal
[inaudible 00:40:48]
binomial.
Just to finish...
Just
to
finish
the
rap-up
is
the
lognormal
distribution
fits
very
well
the
blood
loss
volume
distribution
so
why
not
to
use
it?
Using
this
fact,
the
estimates
of the
risks are
much
more
precise.
We
even
show
that
our
big
trial
was
saved
in
some
sense
by
showing
non-
inferiority
of
carbetocin,
using
the
lognormal.
We
are
very
happy
to
communicate
to
you
that
a
new
trial
is
already
underway
now
using
the
lognormal.
Now
this
trial
would
not
come
to
life
because
we
don't
have
money
for
30,000
people.
But
since
we
are
using
only
less
than
4000,
that
made
the
trial
possible.
It's
underway
now.
It's
for
treatment,
not
for
prevention
like
they're
of
others.
That's
what
I
had
to
tell
you.
Thank
you
very
much.
