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Getting the help of SAS in
Clinical Trial setting: Monitoring
and Simulations
Presented By:
Mehmet Kocak
Phase I Clinical Trials
Objective is to find a maximum tolerated dose
(MTD) of a new cytotoxic drug
– MTD is not really the “maximum” tolerated dose
but rather the highest dose that yields manageable
side effects.
– This dose is called the “target” dose.
– Think of MTD as the target dose which is the dose
that yields a specified probability of toxicity, e.g.
25%.
Continual Reassesment Method(CRM)
Bayesian dose-finding method developed by
O’Quigley et al (Biometrics, 1990)
Statistical model is used to estimate the
relationship between dose and probability of
toxicity (dose-toxicity)
After study opens, the model is fit to the actual
data and used to estimate the target dose.
Continual Reassesment Method(CRM)
Go to Next Dose
Start with the first Dose
Add More
Decision
Go to Previous Dose
First Dose is too toxic.
Continual Reassesment Method(CRM)
Obs
Dose
n
r
MTD
MidDose
Decision
1
120
2
0
1216.67
180 GOTONEXT
2
240
2
0
1165.46
360 GOTONEXT
3
480
3
0
1243.45
600 GOTONEXT
4
720
3
2
539.85
960 GOTOPREV
5
480
1
0
560.85
600 ADDMORE
6
480
1
0
578.19
600 ADDMORE
7
480
1
0
592.62
600 ADDMORE
8
480
1
0
604.67
600 GOTONEXT
9
720
1
0
654.01
960 ADDMORE
10
720
1
0
703.34
960 ADDMORE
11
720
1
0
753.23
960 ADDMORE
12
720
1
0
803.51
960 ADDMORE
13
720
1
1
679.89
960 ADDMORE
CRM – Statistical Model
Logistic function is used frequently to model
the dose-toxicity relationship.
Don’t know the true relationship between dose
and the probability of toxicity.
Here are three sample logistic curves:
CRM – Statistical Model
Relationship Between Dose and Toxicity Based on the Logistic Function
1.0
Curve 2
Probability of Toxicity
0.8
0.6
Curve 1
0.4
Curve 3
0.2
Dose
Prob. Tox.
Curve 1
Prob. Tox.
Curve 2
Prob. Tox.
Curve 3
2%
25%
1%
235
6%
95%
3%
472
44%
100%
16%
628
78%
100%
35%
0.0
100
300
500
Dose
100
700
900
CRM – Statistical Model
If you don’t know the true relationship between dose
and toxicity, how do you estimate the MTD?
– Use the actual data from the study to estimate the
dose-toxicity curve
– Borrow data from other experiences
What is the target dose of interest?
– Dose that has 25% toxicity
What is the proposed dose-toxicity relationship?
– Don’t have actual data when the study opens
– Need idea about the relationship between dose and
toxicity to initiate the model fitting (priors)
CRM – Priors
Other Studies
– Adult study
– Study in different population
Guess
– Quantify clinical intuition about drug behavior at
high and low doses
What dose would you guess has 90% toxicity?
What dose would you guess has 10% toxicity?
CRM - Example
Investigator wants to open a phase I study with 4 dose levels
– 100 mg/m2, 235 mg/m2, 472 mg/m2, and 628 mg/m2
Need priors to initiate model
– Prior studies
Has there been a previous phase I study using this drug?
– Investigator’s clinical intuition about high and low doses
What dose would you expect 90% toxicity?
What dose would you expect 10% toxicity?
– Reduce the lowest dose by half for the low prior and
increase the highest dose by half for the high prior
50 for low prior and about 950 (628 + 314) for high
prior
Modified Continual Reassesment
(CRM) Software
Programmed by Dr. Steve Piantadosi
– Nice interface
– Has problems
Required data for the model to run:
– Dose
– N (number of patients treated)
– r (number of responses (DLTs))
Probability of toxicity
– Weight
Depending on the priors, our initial curve changes tremendously.
Actual Patient Data
Patient Dose Date on Treatment
End of Dose Finding
Period
DLT?
1
100
2/14/03
3/14/03
No
2
100
2/23/03
3/23/03
No
First two patients at Dose 100 mg/m2 did not have
DLTs.
DECISION: ESCALATE TO
THE NEXT DOSE LEVEL
Sample Patient Data (Cont.)
Patient Dose Date on Treatment
End of Dose Finding
Period
DLT?
1
100
2/14/03
3/14/03
No
2
100
2/23/03
3/23/03
No
3
235
3/19/03
4/19/03
No
4
235
4/05/03
5/05/03
No
Note: Next two patients treated at Dose 235 mg/m2
did not have DLTs, either.
DECISION: ESCALATE TO THE NEXT DOSE LEVEL
Sample Patient Data (Cont.)
Patient
Dose
Date on Treatment
End of Dose Finding
Period
DLT?
1
100
2/14/03
3/14/03
No
2
100
2/23/03
3/23/03
No
3
235
3/19/03
4/19/03
No
4
235
4/05/03
5/05/03
No
5
472
4/21/03
5/12/03
Yes
Note: Patient-5 had a DLT. We will immediately reestimate the MTD based on the current toxicity
information.
DECISION: GO BACK TO Dose Level 235.
History of CRM Decision
Two Step Simulation
Remember that
we decided to
de-escalate
from Dose 472
mg/m2 to 235
mg/m2.
What can we
say about the
future
decisions?
Not the actual doses
under investigation!
Two Step Simulation with SAS
Function=
“move”
Function=
“draw”
Thanks to SAS ANNOTATE Facility
Simulation Study with SAS:
Does CRM really Works?
Go to Next Dose
Start with the first Dose
Add More
Decision
Go to Previous Dose
First Dose is too toxic.
Simulation Study in SAS
Various Dose-toxicity relationships
Iterative Procedure, which is most likely
different for each simulation run;
You cannot sample the whole data at once;
10,000 simulations in each setting
Preserving all necessary components of runs
for summarization
Huge data sets, complicated algorithm.
The Brain of the Simulation in SAS
If the current dose is safe
%next:
Start with the first Dose
If you need more data
%addmore:
%Decision:
Processes…
If First Dose is too toxic
Or you find the MTD,
%goto…
%exit:
If the current dose is not safe,
%prev:
Simulation Study in SAS
%decision:
--- DATA STEPS ----- SEVERAL %IF AND % GOTO STATEMENTS--%if &maxcount>=6 and &decision=GOTONEXT and &dose<&nofdl %then %goto
next;
%else %if &maxcount>=6 and &decision=GOTOPREV and &dose^=1 %then %goto prev;
%else %if &decision=GOTONEXT and &dose=&nofdl %then %goto addmore;
%else %if &decision=GOTOPREV and &dose=1 %then %goto exit;
%else %if &decision=GOTONEXT %then %goto next;
%else %if &decision=ADDMORE %then %goto addmore;
%else %if &decision=GOTOPREV %then %goto prev;
%next: %let dose=%sysevalf(&dose+1); %let ctr=%sysevalf(&ctr+1); %goto decision;
%addmore: %let ctr=%sysevalf(&ctr+1); %goto decision;
%prev: %let dose=%sysevalf(&dose-1); %let ctr=%sysevalf(&ctr+1); %goto decision;
%exit:
A paper submitted for publication
Modified Continual Reassessment Method versus the Traditional Empirically-Based
Design for Phase I Trials in Pediatric Oncology: Experiences of the Pediatric Brain
Tumor Consortium
Arzu Onar*, Mehmet Kocak, James M. Boyett
Biostatistics Department, St. Jude Children’s Research Hospital, 332 North Lauderdale St.
Mail Stop 768 Memphis TN 38105
* Corresponding author: Arzu Onar Biostatistics Department, St. Jude Children’s Research
Hospital, 332 North Lauderdale St. Mail Stop 768 Memphis TN 38105. Email:
[email protected]. Tel: 901 495 5499. Fax: 901 544 8843.
References
Piantadosi S, Fisher JD, Grossman S. Practical
implementation of a modified continual
reassessment method. Cancer Chemother
Pharmacol, 41:29-436, 1998.
Goodman SN, Zahurak ML, Piantadosi, S.
Some practical improvements in the continual
reassessment method for phase I studies.
Statistics In Medicine, 14:1149-1161, 1995.