C t ib ti off Biostatistics
Contribution
Bi t ti ti to
t
Designing Clinical Trials
Satoshi Morita, PhD
Dept. of Biostatistics and Epidemiology,
Yokohama City University
An elephant in the living room
A clinical
li i l trial
i l iin an uncommon di
disease
Low incidence
El#1
Slow accrual
Conducting a clinical trial in
such a disease often infeasible
In addition, if low mortality and morbidity
Impractical to achieve adequate power
Another elephant in the living room
N universally
No
i
ll accepted
d endpoint
d i
Malignant ascites secondary to gastric
Cancer: severe end-stage manifestation
of the disease,
disease
El#2
poses particular problems to clinicians
in terms of providing suitable treatment.
However NO universally accepted measure for
However,
assessing response to therapy.
Why? No measurable lesions!!
Unable to be assessed by the RECIST criteria.
Today I would like to talk about
Today,
about…
How biostatistics can contribute to designing
and analyzing clinical trials in uncommon
diseases?
What biostatisticians have done and are
doing for it?
Examples: Cancer clinical trials
pediatric cancer, sarcomas
malignant ascites (gastric cancer)
Uncommon disease
Ex. Pediatric cancer
Pediatric doctors often rely on evidence from adult
clinical trials.
It is natural to consider “borrowing strength” from
previous or simultaneous adult studies.
Prior Information
External data
Clinical trial designs
Dose-level(s)
p size required
q
Sample
Trial monitoring
(Stop/go decision)
Statistical plan
Ex. Pediatric phase I trial in
metastatic sarcoma
Patients with
stage IV Ewing’s
Ewing s sarcoma,
sarcoma rhabdomyosarcoma
age 1 – 30 yrs
Objective:
to establish “pediatric dosage” of topotecan
in combination with Cyclophosphamide (fixed dose)
and Melphalan (fixed dose)
Using prior information / knowledge
Prior information:
Optimal dose in adults: 4 mg/m2/day
Clinical trials in other cancers
(ovarian cancer
cancer, multiple myeloma
myeloma,…))
In the present study,
p
3.5 and 3.0
Two dose levels of topotecan:
 # of patients: at most 20 (Pt accrual: >2 yrs)
What if more than two levels?
Much more patients!
Study design / statistical method
Continual reassessment method
Much longer time!!
A “Bayesian” approach
A phase II trial for sarcoma
Thall et al.
al (2003) present a Bayesian study design
for a single-arm phase II trial to examine the
efficacy of the targeted drug imatinib for sarcoma
with many subtypes.
Sarcoma is uncommon
The goal was to construct a design that allowed
the efficacy of imatinib to be evaluated
in the multiple subtypes.
Subgroups?
Borrowing strength!!
Hierarchical Bayesian model
10. Angiossarcoma
9 Peripheral nerve
9.
sheath saarcoma
8 Rhabdomyo
8.
sarcoma
7. Osteosaarcoma
6. Ewing’s sarcoma
5. Liposarccoma
4. Fibrosarrcoma
3 Malignant fibrous
3.
histiocyytoma
2. Leiomyoosarcoma
1. Synovial sarcomaa
Borrowing strength (cont’d)
(cont d)
G / No
Go
N go decision
d i i
Conventional
design
(repeating separate trials)
0/8
Hierarchical
B
Bayesian
i model
d l
0/8
0/5 1/4 3/7 2/8
3/9 2/8 1/8 2/4 1/5
Stop?
Continue?
Borrowing strength (cont’d)
(cont d)
The number of patients required
required.
Larger # of patients
Conventional
design
(repeating separate trials)
Hierarchical
Bayesian model
More efficient!!
Bayesian approaches!!
Despite
D
it th
the everever-increasing
i
i number
b off new agents,
t
the number of patients available for clinical trials
remains limited.
There is a g
growing
g need for statistical methodologies
g
that can “rapidly
“rapidly and efficiently”
efficiently” evaluate the
clinical efficacyy and safety
y of new agents.
g
Bayesian approach
Why Bayesian approaches?
B
Bayesian
i methods
h d iincorporate
previous study data
pre--clinical (animal) data
pre
-----as prior
i iinformation.
f
ti
Study design and Data analysis
Bayesian
y
approach:
pp
Updating a prior by observed data
Previous studies
Clinicians’ experience
Prior
＋
Data
P t i
Posterior
Observed data
Trial monitoring
( (external) data collected Go / No go decision
(+
simultaneously)
Ap
p--value: data from 12 patients
Clinically NOT meaningful: response rate < 25%
4 responses
12 patients
ti t
Response
p
rate = 33%
p-value = 0.36
NOT significant? Negative data?
P-value
GOD for clinicians?
Source: Rimm &Bortin,,
Biomedicine, 1978
ＥＢＭ
P-value
DEVIL for
clinicians?
li i i
?
Source: Rimm &Bortin,
Biomedicine, 1978
Bayesian
analysis of
Prior distribution
response rate
High posterior probability (Response rate > 0
0.25)
25)
Go to the next phase trial
Prior distribution
Response rate
Prior → Posterior
Response rate
Posterior distribution
Prior + 1 / 3 pts
Posterior probability (Response rate > 0
0.25)
25)
= 60%
Response rate
Prior → Posterior
Response rate
Posterior distribution
Prior + 4 / 12 pts
P t i probability
Posterior
b bilit (R
(Response rate
t >0
0.25)
25)
= 80%
Response rate
Developing a new endpoint to assess
response to
t therapy
th
for
f malignant
li
t ascites
it
M li
Malignant
t ascites
it
brings a rapid deterioration
(Pain, loss of appetite, obstructive symptoms, dyspenea)
A significant negative impact on performance status
and quality of life of patients
Patient centered response is therefore
Patient-centered
more important than tumor response.
How to measure the response
p
to therapy?
py
“G ld standard”:
“Gold
t d d”
Ascitic volume changes by 3D-CT
((originally
i i ll used
d tto estimate
ti t th
the volume
l
off organs
before and after transplantation)
But, practical limitation…
time- and resource-demanding
(special equipment, trained radiologists (over 30 mins))
How to measure the response to therapy?
(cont’d)
(cont d)
Approximating the ascitic volume:
The Five-Point method,
b
based
d on conventional
ti
l CT iimages
BUT, this alternative does not account for
patient-centered
centered changes”
changes
“patient
Use the broadly accepted concept of
Clinical Benefit Response”
Response
“Clinical
in pancreatic cancer as a prototype!!
Frequency of
abdominal
paracentesis
Girth of
abdomen
Diuretic
consumption
Ascites fluid
Responder
Stable
Non-responder
Decrease in ascites fluid
No change
Increase in ascites fluid
PS
Responder
improvement in PS
Stable
no change
Non-responder
deterioration in PS
Clinical Benefit ResponseResponse-Gastric Cancer
Ascites fluid
＋
＋
PS
Stable
Stable
−
Responder
Non--responder
Non
−
26
Still challenging
challenging…
Bayesian approaches have the potential of
making clinical trials in uncommon diseases
feasible.
feasible
“FDA Guidance for the Use of Bayesian Statistics
in Medical Device Clinical Trials”
p
external information
However,, how to incorporate
is NOT fully established.
New endpoint development
Useful and appealing, but many steps and long
time to establish a new endpoint.