Transcript Adaptive Dose-Response Studies

Adaptive Dose-Response Studies
Inna Perevozskaya
Merck & Co,Inc.
10/27/2006
Philadelphia ASA Chapter Meeting
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Acknowledgement:
PhRMA Adaptive Designs Working Group

Co-Chairs:
Michael Krams
Brenda Gaydos
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Authors:
Keaven Anderson
Suman Bhattacharya
Alun Bedding
Don Berry
Frank Bretz
Christy Chuang-Stein
Vlad Dragalin
Paul Gallo
Brenda Gaydos
Michael Krams
Qing Liu
Jeff Maca
Inna Perevozskaya
Jose Pinheiro
Judith Quinlan

Members:
Carl-Fredrik Burman
David DeBrota
Jonathan Denne
Greg Enas
Richard Entsuah
Andy Grieve
David Henry
Tony Ho
Telba Irony
Larry Lesko
Gary Littman
Cyrus Mehta
Allan Pallay
Michael Poole
Rick Sax
Jerry Schindler
Michael D Smith
Marc Walton
Sue-Jane Wang
Gernot Wassmer
Pauline Williams
Upcoming DIJ Publications by PhRMA working
group on adaptive designs
1.
P. Gallo, M. Krams
Introduction
2.
V. Dragalin
Adaptive Designs: Terminology and Classification
3.
J. Quinlan, M. Krams
Implementing Adaptive Designs: Logistical and Operational Considerations
4.
P. Gallo.
Confidentiality and trial integrity issues for adaptive designs
5.
B.Gaydos, M. Krams, I. Perevozskaya, F.Bretz; Q. Liu, P.
Gallo, D. Berry; C. Chuang-Stein, J. Pinheiro, A. Bedding.
Adaptive Dose Response Studies
6.
J. Maca, S. Bhattacharya, V. Dragalin, P. Gallo, M. Krams,
Adaptive Seamless Phase II / III Designs – Background, Operational Aspects, and
Examples
7.
C. Chuang-Stein, K.Anderson, P. Gallo, S. Collins.
Sample Size Re-estimation: A Review and Recommendations
Dose-Response Paper Overview
1.
2.
3.
4.
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6.
7.
8.
Motivation: Challenges in evaluation of doseresponse
Summary of key recommendations from PhRMA
dose-response workstream
Overview of traditional dose-response designs
Overview of adaptive dose-response methods in
early exploratory studies
Adaptive Frequentist approaches for late stage
exploratory development
Developing a Bayesian adaptive dose design
Monitoring issues and processes in adaptive
dose-response trials
Rolling dose studies
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1. Motivation: Challenges in Evaluation
of Dose-Response
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Insufficient exploration of the dose response is often a key shortcoming of
clinical drug development.
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Initial proof-of-concept (PoC) studies often rely on testing just one dose
level (e.g. the maximum tolerated dose)
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Additional exploration of dose-response typically done later (Phase IIb
trials)
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Adaptive designs offer efficient ways to learn about the dose response
and guide decision making (dose selection/program termination)
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It is both feasible and advantageous to design a PoC study as an adaptive
dose response trial.
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Continuation of a dose response trial into a confirmatory stage in a
seamless design is a further opportunity to increase information on the
right dose(s)
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Adaptive dose-response trial may offer deduction in the
development timeline
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1. Motivation (cont.)
Efficient learning about the dose response
earlier in development could reduce
overall costs and provide better
information on dose in the filing package
 This review primarily focuses on phase Ib
and II study designs
 Applicable to endpoints that support filing
or are predictive of the filing endpoint
(e.g. biomarkers).
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2. Key recommendations of the PhRMA
Adaptive Dose-Response workstream
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Consider adaptive dose response designs in
exploratory development.
Consider adaptive dose response designs to
establish proof-of-concept
Whenever possible use an approach that
incorporates a model for the dose response.
Consider seamless approaches to improve the
efficiency of learning
Define the dose assignment mechanism
prospectively and fully evaluate its operational
characteristics through simulation prior to
initiating the study.
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Key recommendations of the PhRMA
Adaptive Dose-Response workstream (cont.)
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Stop the trial at the earliest time point when
there is enough information to make the decision.
A committee must monitor the study on an
ongoing basis to verify that the performance of
the design is as expected
Engage the committee early in scenario
simulations prior to protocol approval.
Leverage the information from disease state and
exposure-response models to design studies.
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3. Traditional methods to explore doseresponse
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Fixed-dose parallel-group designs
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Target:
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average population response at a dose
shape of the population dose response curve
Downside:
For both
efficacy
and
safety
potential to allocate a fair number of patients to several
non-informative doses
 sample size considerations often limit the number of doses
feasible to explore
Primarily
 Fixed dose cross-over design,
aimed at
learning
 Forced titration design,
about
 Optional titration design
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4. Adaptive dose-response methods for
early exploratory studies
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Review traditional phase I designs with respect to
estimation of the Maximum Tolerated Dose (MTD)
Discuss novel adaptive design approaches aimed
at improving the relatively poor performance of
traditional designs
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Majority of these methods originated from oncology
There is methodological overlap with other dose
response methods (late stage)
applicability can be generalized from MTD determination
to learning about the dose response profile for any
defined response (e.g. tolerability, safety or efficacy
measure)
More work generally needs to be done to extend
applicability beyond the area of cancer research
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Specifics of oncology Phase I trials
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Typically very small, uncontrolled sequential studies in patients
Binary outcome: toxicity response
Designed to determine the maximum tolerated dose (MTD) of the
experimental drug
Design challenges are driven by severe side effects of cytotoxic
drugs, limited number of patients available
Certain degree of side effects is acceptable, but every effort
should be made to minimize exposure to highly toxic doses
Balance between individual and collective ethics: maximum
information from the minimal number of patients
Be open-minded: the designs presented here originated in Phase
I oncology
But can be potentially be useful for other early development trials
(e.g. efficacy assessment; dose-ranging, POC)
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Statistical modeling of efficient learning
about MTD
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Schacter et al. (1997): “a well-designed phase I study
will identify a dose at which patients can be safely
treated and one which can benefit the patient”.
Assumption: monotone relationship between the dosage and
response
Two different philosophies in MTD definition:
1.Risk of toxicity is a sample statistic, identified by the doses
studied
e.g., 3+3 design: MTD is highest dose studied with < 1/3
toxicities
2.Risk of toxicity is a parameter of a dose-response model
e.g., dose associated with 30% incidence of toxic response
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Two different approaches in designing phase I clinical trials
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Summary of available methods for phase I clinical
trials (Rosenberger and Haines, 2002)
1. Conventional (standard) method
al., 1994)
(Simon et al., 1997; Korn et
2. MTD as a quantile vs. conventional method
a) Random walk rules (RWR)
b) Continual reassessment method (CRM)
O’Quigley, Pepe, Fisher (1990)
c) Escalation with overdose control (EWOC)
Babb, Rohatko, Zacks (1998)
d) Decision-theoretic approaches
Whitehead and Brunier (1995)
Bayesian Methods
Durham and Flournoy (1994)
e) Bayesian sequential optimal design
Haines, Perevozskaya, Rosenberger (2003)
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Learning about MTD: current and novel
designs summary
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Key feature: Prior response data used for sequential
allocation of dose/treatment to subsequent (group of)
subjects
Up-and-down type designs: utilize only last response in
decision rule
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Bayesian type designs: all previous responses from the
current study are utilized
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Conventional 3+3 designs for cancer (traditional)
Random-walk-rule designs
Continual Reassessment Method
Other Bayesian approaches
Common goal: limit allocation to extreme doses of little
interest
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1. Current practice in MTD estimation:
‘conventional’ 3+3 design for cancer
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Designed under philosophy that MTD is identifiable from
the data
Patients treated in groups of 3
Designed to screen doses quickly; no estimation involved
Probability of stopping at incorrect dose level is higher than
generally believed (Reiner, Paoletti, O’Quigley; 1999)
First 3 patients
treated at initial dose
If no toxicities,
moves to next
higher dose
If ≥2 toxicities,
moves to next
lower dose
If 1 toxicity,
stays at the
current dose
If 1 toxicity out
of 6 treated, moves
to next higher dose
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If ≥2 toxicities out
of 6 treated, moves
to next lower dose
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3+3 design for cancer
Pros (+) & Cons (-)
+ has been around for a long time, properties well documented
- estimates MTD at ~ 20% toxicity level
+ Weili He, et al., show how to estimate MTD at intended 30%
level
- derived estimates conditional on doses yielded by design
not well suited to yield any efficacy info (not suitable for
estimating any rate of response other than 30%;
yields little info above MTD
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2. Random Walk Rules (RWR) or
“biased coin” designs
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Nonparametric model-based approach: MTD is a
quantile of a certain dose-response distribution but
there is no underlying parametric family.
Biased-coin design generalizes the up-and-down
approach of the conventional method: can target any
response rate of interest (not only 30%).
Similarity: patients are treated sequentially with the
next higher, same, or next lower doses
Difference: rule for dose escalation (probability of next
dose assignment depends on previous response)
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2. Random Walk Rules (cont.)
Patient j-1
assigned to
dose di
Toxic
response
Non-toxic
response
Patient j
assigned
to dose di-1
Flip a biased
coin
Pr (heads)=b<1/2
HEADS: next
assignment di+1
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TAILS: next
assignment at di
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Random Walk Rule Example
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An example of particular case of RWR with P=1
Developed in MRL in 1980’s for efficacy dose-ranging
studies
called “up-and-down design” then
Applied to simulated dose-ranging study in dental pain (full
description in back-ups)
Demonstrates ~50% reduction in sample size without big
loss in precision of estimates of dose-response compared to
parallel group design
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Pros (+) & Cons (-) of Random Walk Rules
+ Simple and intuitive to explain; easy to implement
+ flexible enough to target any level of response
+ assigned doses cluster around quantile of interest ( MTD)
- Consequently, some patients will be assigned above the MTD
(concern for oncology only)
+ minimizes observations at doses too small or too large, in
comparison to randomized design
- derived estimates conditional on doses yielded by design
+ derived info useful to design definitive studies
+ simulations indicate estimated response proportion at each
dose is unbiased
+ Have workable finite distribution theory
+ Reliable MTD estimates can be obtained using isotonic
regression
- May not converge to MTD as fast as some Bayesian methods
(wider spread of doses)
+ But, for practical considerations (safety), slow dose escalation
is desirable
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3. Designs based on Bayesian methods
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Continual Reassessment Method (CRM)
Escalation With Overdose Control (EWOC)
Decision Theoretic Approaches
Bayesian Optimal Sequential Design
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Continual Reassessment Method (CRM)
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Most known Bayesian method for Phase I trials
Underlying dose-response relationship is described by a 1parameter function
For a predefined set of doses to be studied and a binary
response, estimates dose level (MTD) that yields a particular
proportion (P) of responses
CRM uses Bayes theorem with accruing data to update the
distribution of MTD based on previous responses
After each patient’s response, posterior distribution of model
parameter is updated; predicted probabilities of a toxic
response at each dose level are updated
The dose level for next patient is selected as the one with
predicted probability closest to the target level of response
Procedure stops after N patients enrolled
Final estimate of MTD: dose with posterior probability closest
to P after N patients
The method is designed to converge to MTD
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Innovative Clinical Drug Development
Conference
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Continual Reassessment Method (cont.)
Choose
initial estimate
of response
distribution
& choose
initial dose
Stop.
EDxx = Dose w/
Prob. (Resp.)
Closest to
Target level
Obtain next
Patient’s
Observation
Next Pt. Dose
= Dose w/
Prob. (Resp.)
Closest to
Target level
Update Dose
Response Model
& estimate
Prob. (Resp.)
@ each dose
no
Max N
Reached?
yes
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CRM Design example (1)
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Post-anesthetic care patients received a single IV dose of
0.25, 0.50, 0.75, or 1.00 μg/kg nalmefene.
Response was Reversal of Analgesia (ROA) = increase in
pain score of two or more integers above baseline on 010 NRS after nalmefene
Patients entered sequentially, starting with the lowest
dose
The maximum tolerated dose = dose, among the four
studied, with a final mean posterior probability of ROA
closest to 0.20 (i.e., a 20% chance of causing reversal)
Modified continual reassessment method (iterative
Bayesian proc) selected the dose for each successive pt.
as that having a mean posterior probability of ROA
closest to the preselected target 0.20.
1-parameter logistic function for probability of ROA used
to fit the data at each stage
Dougherty,et al. ANESTHESIOLOGY (2000)
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CRM example (1) results
Dose
(ug/kg)
0.25
# pts.
4*
# w/ ROA
mean
median
post.
post.
% w/ ROA
prob. ROA prob. ROA
0
0%
0.09
0.11
0.50
18
3
17%
0.18
0.21
0.75
3
2
67%
0.37
0.41
1.00
0
-
-
0.79^
0.80^
(MTD)
* including the 1st patient treated
(MTD), i.e., estimated mean posterior probability closest to 0.20
target
^ extrapolated
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Posterior ROA Probability
(with 95% probability intervals)
CRM example (1) results
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1.0
0.8
0.6
0.4
0.2
0.0
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Escalation with overdose control (EWOC)
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Assumes more flexible model for the dose-response curve in
terms of two parameters:
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MTD
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probability of response at dose D1
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Similar to CRM in a way the distribution it updates posterior
distribution of MTD based on this two-parameter model
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Introduces overdose control: predicted probability of next
assignment exceeding MTD is controlled (Bayesian feasible
design)
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Assigns doses similarly to CRM, except for overdose control;
this distinction is particularly important in oncology
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EWOC is optimal in the class of the feasible designs
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Decision-theoretic approaches
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Wide class of methods characterized by application of
Bayesian Decision Theory to address various design
goals:
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shorter trials, reducing number of patients, maximizing
information, reducing cost etc.
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Similar to CRM: parametric model-based approach
where the posterior distribution of model parameters
is updated after addition of each new patient
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Uses gain functions depending on the desired goal
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Constructs a design maximizing the gain function using
the set of “action” (pre-selected doses).
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Decision-theoretic approaches (cont.)
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Whitehead and Brunier (1995): Loss function minimizes
asymptotic variance of MTD
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Two-parameter model with for dose response with prior
distributions on the parameters
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Posterior distribution estimates of the 2 parameters used to
derive next dose, i.e., that estimated to have desired response
level
Most versatile:
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CRM and Bayesian D-optimal designs can be written as special
cases
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Can be extended for simultaneous assessments of efficacy &
toxicity
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Patterson et al (1999) and (Whitehead et al (2001) extend this
methodology in looking at pharmacokinetic data with two gain
functions.
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Bayesian D-Optimal Sequential design
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The methodology is similar to decision-theoretic
approach, i.e. principally concerned with efficiency of
estimation
A two parameter model is used with logistic link
function defining the dose response curve
Based on formal theory of optimal design (Atkinson
and Donev, 1992)
Optimality criterion chosen to minimizes variance of
posterior distribution of model parameters
Similar to EWOC, a constraint is added to address the
ethical dilemma of avoiding extremely toxic doses
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Bayesian D-Optimal Sequential design (cont.)
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General methodology developed for the case when the
dose space is unknown (continuous dose space)
Case when doses are fixed in advance is particularly
important in practice (discrete dose space)
Sequential procedure developed consisting of:
 “Pilot” design stage for seeding ( small group of
subjects; dose assignments based on prior information
only)
 Subsequent assignments for each patient chosen in
accordance with D-optimality criterion to maximize
information from the design
 Posterior updated after each response and affects future
dose assignments
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Simultaneous assessment of efficacy
and toxicity
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Penalized D-optimal designs
(V. Dragalin and V. Fedorov, 2005)
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Non-Bayesian
Accomplish “learning” by sequential updating of
likelihood function afetr each patient’s response
D-optimality criterion (maximizing Fisher’s information)
is “ driving” the design
Optimization subject to constraint (reflecting ethical
concerns, cost, sample size etc.)
Flexibility of constraints and bivariate model allow to
address a number of questions involving efficacy and
safety dose-response curves simultaneously
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5. Adaptive Frequentist approaches for
late stage exploratory development
These designs more typically applicable to
phase II studies
 Strongly control type I error rate
 2 sources of multiplicity in adaptive doseresponse trials:
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Multiple comparisons of various doses vs.
control
Multiple interim looks at the data
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5. Adaptive Frequentist approaches for late
stage exploratory development (cont.)
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Classical group-sequential design (Jennison &
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Adaptive design (Jennison & Turnbull, 2005; Bauer &
Turnbull, 2000)
 Planned SS or information may be reduced if trial (or
arm) stopped early
 At each interim looks test statistics compared to predetermined boundaries
 Multi-arm trials: Stallard & Todd, 2003
Brannath, 2004)
 More flexible in adaptation->more suitable for multistage framework
 Allowed adaptations may include:  sample size,
modifying patient population, adapting doses
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5. Adaptive Frequentist approaches for late
stage exploratory development (cont.)
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Further methods
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Use standard single-stage multiplicity
adjustment; some doses may be dropped
(Bretz et. al, 2006)
Combining phase II/III using a surrogate
(Liu & Pledger, 2005; Todd & Stallard, 2005)
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6. Developing a Bayesian adaptive dose
design
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Key feature of all Bayesian methods:
updating information as it accrues
posterior updates)
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Calculating predictive probabilities of future
results
Assessing increment in information about
dose-response curve depending on next dose
assignment
Type I error is not the focus, but can be
studied via simulations
 Downside: computational complexity
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6. Developing a Bayesian adaptive dose
design (cont.)
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Modeling is critical
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Prior distribution is put on model parameters
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In general, no restriction on the model other than it
must have parameters
can be “non-informative”
or incorporate objective historical information
appropriately
simulations used to evaluate robustness w/respect to
choice of prior
As the data accrues, distribution of unknown
parameters is updated (posterior)
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6. Developing a Bayesian adaptive dose
design (cont.)
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Bayesian approaches are standard in Phase I
cancer trials
The methods reviewed earlier were presented in
somewhat restrictive context:
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binary response
strong safety concerns (upper-end dose restriction)
monotonic dose-response curve
specific parametric family for model
More general Bayesian designs are gaining
popularity in phase II dose-ranging studies
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6. Developing a Bayesian adaptive dose
design (cont.)
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Example: ASTIN trial (Berry et. al 2001)
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Flexible model not restricting shape of D-R curve;
non-monotone allowed
Two-stages: dose ranging (15 doses & pbo) and
confirmatory
Incorporated futility analyses
Long-term endpoints were handled via longitudinal
model predicting patient’s long term endpoint using
patient’s intermediate endpoint measurements
Key advantages of BD vs. fixed (in general):
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finds “the right dose” more efficiently
More doses can be considered
If futility analysis is used -> may save resources
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7. Monitoring issues and processes in
adaptive dose-response trials
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All adaptive trials raise issues/concerns about
credibility of the trial conclusions
It is beneficial to have a separate body without
other direct trial responsibilities to review interim
results & recommend adaptations
Other precautions need to be taken
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limiting disclosure of specific numerical information
and/or statistical methodology
These recommendations are especially important
in trials with potential regulatory submission
(even if it is not confirmatory)
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8. Rolling Dose Studies
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Broad class of design and methods that allow
flexible, dynamic allocation of patients to dose
level as the trial progress
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Not a distinct set of methods
Rely more on modeling and estimation rather than
hypothesis testing
Examples include Bayesian, D-optimality, and many
more
Comprehensive simulation project by PhRMA RDS
working group is under way
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Developing different RDS methods
Evaluating and comparing to traditional fixed dose
finding approaches
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9. Conclusions
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Recommend routine assessment of appropriateness of AD
in CDPs
Opportunity to efficiently gain more information about D-R
early in the development (POC) for maximum benefit
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AD are not necessarily always better than traditional fixed
dose
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More streamlined Phase III trial plan
Reduction in timelines & cost
More information at the time of filing
There are many choices of ADs
Extensive planning, simulations, etc.
Added operational and scientific complexity should be justified
Planning is extremely important
Limited examples of AD are available in the literature
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References
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Dragalin V. Adaptive designs: terminology and classification. Drug Inf J. 2006
(submitted)
Rosenberger WF, Haines LM. Competing designs for phase I clinical trials: a review.
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Durham SD, Flournoy N. Random walks for quantile estimation. In Gupta SS,
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O’Quigley J, Pepe M, Fisher L. Continual reassessment method: a practical design
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Dougherty TB, Porche VH, Thall PF. Maximum tolerated dose of Nalmefene in
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Babb J, Rogatko A, Zacks S. Cancer phase I clinical trials: efficient dose escalation
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Patterson S, Jones B. Bioequivalence and Statistics in Clinical Pharmacology
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Whitehead J, Zhou Y, Stevens J, Blakey G. An evaluation of a Bayesian method of
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2004;14(4):969-983.
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References (cont.)
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Haines LM, Perevozskaya I, Rosenberger WF. Bayesian optimal designs for phase I clinical trials.
Biometrics 2003;59:561-600.
Dragalin V, Fedorov V. Adaptive designs for dose-finding based on efficacy-toxicity
response. Journal of Statistical Planning and Inference 2005;136:1800-1823.
Jennison C, Turnbull BW. Group Sequential Methods with Applications to Clinical
Trials. London: Chapman and Hall;2000.
Stallard N, Todd S. Sequential designs for phase III clinical trials incorporating
treatment selection. Stat Med. 2003;22:689-703.
Jennison C, Turnbull BW. Meta-analyses and adaptive group sequential designs in
the clinical development process. J Biopharm Stat. 2005;15:537-558.
Bauer P, Brannath W. The advantages and disadvantages of adaptive designs for
clinical trials. Drug Discovery Today 2004;9(8):351-357.
Bretz F, Schmidli H, König F, Racine A, Maurer W. Confirmatory seamless phase
II/III clinical trials with hypothesis selection at interim: General concepts. Biom J.
2006 (in press).
Liu Q, Pledger WG. Phase 2 and 3 combination designs to accelerate drug
development. J Am Stat Assoc. 2005;100:493-502.
Todd S, Stallard N. A new clinical trial design combining phases II and III:
sequential designs with treatment selection and a change of endpoint. Drug Inf J.
2005;39:109-118.
Berry DA, Müller P, Grieve AP, Smith M, Parke T, Blazek R, Mitchard N, Krams M.
Adaptive Bayesian Designs for Dose-Ranging Drug Trials. In Gatsonis C, Carlin B,
Carriquiry A ed. Case Studies in Bayesian Statistics V 99-181. New York: SpringerVerlag;2001.
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Questions?
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Backups
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Back-up set 1: Up&Down Design
Definition






Yields distribution of doses clustered around dose with
50% responders (ED50)
1st subject receives dose chosen based on prior
information
Subsequent subjects receive next lower dose if previous
subject responded, next higher dose if no response
Data Summaries:
 proportion of responders at each dose
 continuous data via summary statistics by dose
Inference based on conditional distribution of response
given the doses yielded by the dosing scheme
5 MRL examples from 1980’s
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Up & Down Design
Simulated from Past Trial Results

Single-dose dental pain study (total 399 patients)
 51 placebo patients
 75 Dose 1 patients
 76 Dose 2 patients
 74 Dose 3 patients
 76 Dose 4 patients
 47 ibuprofen patients

Primary endpoint is Total Pain Relief (AUC) during 0-8 hours post
dose (TOPAR8)

Up&Down design in sequential groups of 12 patients sampled
from study results sorted by AN within treatment.
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Simulated Up&Down Design
from completed Dental Pain Study

Sequential groups of 12 patients (3 placebo, 6 test drug, 3
ibuprofen)

First group receives Dose 2

Subsequent group receives next higher dose if previous group is
non-response, next lower dose if response

Response (both conditions satisfied):
 Mean test drug – mean placebo ≥ 15 units TOPAR8
 Mean test drug – mean ibuprofen > 0

Algorithm continues until all ibuprofen data exhausted
 originally planned precision for ibuprofen vs placebo
 (16 groups = 191 total patients)
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Dental Pain Study Complete Results
Mean TOPAR8
30
25
20
15
10
5
0
placebo
Dose 1
Dose 2
Dose 3
Dose 4
Active
Control
Treatment Group
Parallel Group Design
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Simulated Up&Down Results
from Dental Pain Study data
(1st 8 Groups in sequence)
Test drug
Dose
mean of 3
placebo
mean of 6
Test drug
mean of 3
ibuprofen
Resp/
Non-Resp
Dose 2
14.75
17.92
18.50
NR
Dose 3
7.33
25.42
23.42
R
Dose 2
3.58
21.75
21.42
R
Dose 1
2.50
16.42
21.75
NR
Dose 2
3.17
24.13
19.58
R
Dose 1
8.75
21.88
14.67
NR
Dose 2
6.42
19.71
23.83
NR
Dose 3
7.00
23.50
18.75
R
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Simulated Up&Down Results
from Dental Pain Study data
(last 8 Groups in sequence)
Test drug
dose
mean of 3
placebo
mean of 6
Test drug
mean of 3
ibuprofen
Resp/
Non-Resp
Dose 2
10.75
25.21
21.75
NR
Dose 3
14.33
21.54
19.08
NR
Dose 4
0.00
24.29
11.33
R
Dose 3
0.50
21.17
18.42
R
Dose 2
4.50
26.79
18.00
R
Dose 1
2.50
5.88
16.67
NR
Dose 2
0.00
24.63
13.75
R
Dose 1
0.00
15.25
25.13
NR
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Dental Pain Randomized Design
vs Up&Down Design Results
Mean Responce with 95% CI
32
28
24
20
16
12
8
4
Complete (N=399)
Up&Down (N=191)
0
Placebo
Dose 1
Dose 2
Dose 3
Dose 4
Ibuprofen
Treatment Group
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Dental Pain Randomized Design
vs Up&Down Design Results
Number of Patients Studied
Complete Trial N=399, Up&Down Design N=191
60
40
76
75
80
51 48
76
74
47 47
42
24
24
6
20
0
placebo
Dose 1
Dose 2
Dose 3
Dose 4
Active
Control
Treatment Group
parallel group design
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Conclusions from Simulated Up&Down Design in
Dental Pain

Up&Down design is viable for dose-ranging in Dental Pain
 Yields similar dose-response information as parallel group
design

Can use substantially fewer patients than parallel group design

Logistics of implementation more complicated than usual parallel
group design
 Can be accomplished in single center or small number of
centers
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Back-up slide set 2: D-optimal design implemented in a
user-friendly software: iDose (Interactive Doser)

Web-based application is available to any workstation equipped
with a web browser (Rosenberger et al., Drug Information Journal,
2004)

Nothing to install/maintain on the client side

Integration with other software for patient information is easy

Service-oriented architecture of web-based application

Addition of high-value services is easy to deploy, update , and
maintain

Service can be offered by external providers

Clinician access control must be implemented

Low security requirements: no actual patient information
transferred
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iDose Software (cont.)

iDose supports long transactions

Dose and toxicity of each patent reported over time

Server keeps track of clients state while waiting for patients
response

Statistical part is implemented in Mathworks Matlab product

Matlab Server product allows Matlab to run on a server as an
external process accessed through Common Gateway Interface

Intermediate stages are preserved as a file

Clinicians use their keyword to retrieve the state where they
left

Any existing access control systems may be layered for
additional security

All parameters entered are checked for validity
Dynamic, context-sensitive help provided for each parameter
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Simulated Bayesian D-optimal design for
ED50 (iDose website)



Osteoarthritis efficacy % good/excellent assumed underlying
distribution
 Dose: 15 30 60 90 120 180 240
 %G/E: 30 40 55 65
75
75 75
Prior estimates: ED25 between 15 and 30 mg
ED50 between 30 and 60 mg
6 patients in Stage 1 for seeding purposes



Optimal Design: 3 pts at 15 mg, 2 at 60 mg, 1 at 90 mg
24 subsequent patients entered sequentially at doses yielding
minimum variance of ED50 estimate
Responses / non-response assigned to approximate targeted
%G/E distribution above
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Simulated Bayesian Optimal Design for
ED50 - Results
Sequence of Doses assigned together with outcome
(1=good/excellent response, 0=not)
AN,Dose(resp)
AN,Dose(resp)
07,
60(1)
15,
08,
15(0)
16, 120(1)
24, 120(0)
09,
60(0)
17,
25,
10,
15(1)
18, 120(0)
26, 120(1)
11, 120(1)
19,
27,
12, 120(0)
20, 120(1)
28, 120(0)
13, 120(1)
21,
29,
14, 120(1)
22, 120(1)
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AN,Dose(resp)
15(0)
15(1)
15(0)
Philadelphia ASA Chapter Meeting
23,
15(0)
15(1)
15(0)
15(0)
30, 120(1)
59
Simulated Bayesian Optimal Design for
ED50 – Summary

Osteoarthritis efficacy % good/excellent assumed
underlying distribution
Dose: 15 30 60 90 120 180 240
assumed %G/E: 30 40 55 65 75
75 75
#Responses:
4
2
1
8
#pts.: 13
0
4
1
12
0
0
observed %: 31
50 100 67
-

Bayesian estimated ED50 = 48.7mg using only 30
patients!!!
However, little info about other doses due to nature of Doptimal design for ED50

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Graphic Summary of Results from
iDose software
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