This document describes a two-stage adaptive clinical trial design to evaluate the efficacy and tolerability of a new drug for post-surgical pain. Stage A will test the drug against placebo and an active control. Stage B will use a maximizing adaptive dose-finding design to estimate the dose with the optimal balance of efficacy and tolerability based on a clinical utility function. Simulation results show this design can accurately estimate the target dose with maximum utility while minimizing patients assigned to less effective or intolerable doses.
2. OUTLINEOUTLINE
• Adaptive design based on customized clinicalAdaptive design based on customized clinical
utility function
• Simulation results document performance• Simulation results document performance
characteristics
R k• Remarks
3. Overall Summary
• Phase 2 trial test drug versus placebo and active control for post surgery• Phase 2 trial test drug versus placebo and active control for post‐surgery
analgesia
• Objectives: PoC + estimate dose regimen with optimal balance between
maximum efficacy and minimum intolerance
• Maximizing adaptive dose‐finding design (Ivanova, 2009) chosen to yield
better quality information
– fewer patients assigned to dose regimens which are ineffective or
intolerableintolerable
• True potential efficacy and tolerability dose‐response (DR) curves were
constructed to span the range of potential DR curves
• Clinical utility function defined to combine all of the efficacy and
t l bilit dtolerability dose‐response curves
• Simulation study evaluated performance characteristics
• Results indicate the maximizing design
– Has high probability to estimate the correct or nearest to correct dose– Has high probability to estimate the correct or nearest to correct dose
with maximum clinical utility (i.e., “target dose”)
– Maximizes assignment of subjects to the target dose
– Minimizes assignment of subject to doses remote from target dose
4. Illustration of Maximizing Design
(Ivanova et al 2009)
Current cohort Next cohort
(Ivanova et al. 2009)
Doses 1 2 3 4 1 2 3 4
Active pair
At given point of the study subjects are randomized to the levels of the
Active pair
of levels
At given point of the study, subjects are randomized to the levels of the
current dose pair and placebo only. The next pair is obtained by shifting the
current pair according to the estimated slope.
5. Maximizing Design Update Rule based on
Standarized Difference
)/1/1(ˆ
)ˆˆ(
2
1 jj
nn
T
)/1/1( 1 jj nn
Let dose j and j+1 constitute the current dose pair.
1 Use isotonic (unimodal) regression or quadratic regression fitted locally1. Use isotonic (unimodal) regression or quadratic regression fitted locally
to estimate responses at all dose levels using all available data
2. Compute T
i. If T > 0.3 then next dose pair (j+1,j+2), i.e. "move up“p (j ,j ), p
ii. If T < ‐0.3 then next dose pair ( j‐1, j), i.e. "move down“
iii. Otherwise, next dose pair ( j, j+1), i.e. “stay”
• If not possible to “move” dose pair, ( j=1 or j=K‐1), change pair’s
randomization probabilities from 1:1 to 2:1 (the extreme dose of
the pair get twice more subjects)
M difi i f hi l (i l di diff ff f T) iblModification of this rule (including different cutoffs for T) are possible
but logic is similar
6. Final Phase 2 Design Choice:
2‐Stage adaptive PoC+Dose‐Findingg p g
• Stage A ‐ PoC: Initial Cohort of 150 patients randomized 1:1:1:1:1:1 to 1 of
4 Test Drug regimens; active control; placebo)
• Enrollment pause for ~1 month while Stage A data are analyzed
• Stage B – Dose‐Finding: Maximizing Design for clinical utility; 2 starting
doses based on the analysis of Stage A
– Patients randomized in ~10 successive weekly cohorts of
approximately 25 patients (depending on weekly enrollment rate)
– Each successive Stage B cohort of ~25 will be randomized 4:8:8:5 to g
placebo, 2 doses of Test Drug, and active control, respectively
– Expected to yield for final analysis ~
• 65 total placebo patients65 total placebo patients
• 75 total active control patients
• > 80‐100 patients on target dose
9. Defining the tolerability cutoffs
( l b l )(>, ~, < on tolerability)
← Increasing Test Drug Tolerability (Relative prevalence of AE)
T has better T tolerability is a T tolerability is a T tolerability isT has better
tolerability than
AC
T‐AC < ‐20
T tolerability is a
bit better than AC
‐20 < T‐AC < 0
T tolerability is a
bit worse than AC
0 > T‐AC > 20
T tolerability is
worse than AC
T‐AC > 20
T efficacy is less 0
Increasing
Test Drug
Effi
T efficacy is less
than AC
T‐AC < ‐1
0
T efficacy is similar
ACEfficacy
(NRS)
↓
to AC
‐0.5 < T‐AC < 0.5
T efficacy is better
than AC
0.5 < T‐AC < 1.5
T efficacy is much
better than AC
100
T‐AC >1.5
10. Potential utility outcome
for each Test Drug group •Exact numbers not importantfor each Test Drug group •Exact numbers not important
•Determines “routing” of next patients
•Gradients are more important
← Increasing Test Drug Tolerability (Relative prevalence of AE)
T has better
tolerability than
AC
T tolerability is a
bit better than AC
20 T AC 0
T tolerability is a
bit worse than AC
0 T AC 20
T tolerability is
worse than AC
T AC 20
T‐AC < ‐20
‐20 < T‐AC < 0 0 > T‐AC > 20 T‐AC > 20
T efficacy is less
than AC
20 0 0 0
Increasing
Test Drug
Efficacy
(NRS)
↓
T‐AC < ‐1
T efficacy is similar
to AC
‐0.5 < T‐AC < 0.5
60 40 0 0
T efficacy is better
than AC
0.5 < T‐AC < 1.5
80 50 40 0
T ffi i h 100 90 50 20T efficacy is much
better than AC
T‐AC >1.5
100 90 50 20
19. Simulation Specifications
• Stage A N=25 on pbo 4 doses Test Drug active control• Stage A N=25 on pbo, 4 doses Test Drug, active control
– Pause enrolment for Stage B starting dose selection
• Stage B 10 cohorts N=25, maximizing design adaptationStage B 10 cohorts N 25, maximizing design adaptation
beginning with 4th Stage B cohort
• Simulated 1000 times for each of 14 selected utility functions
– Normally distributed means per utility function
– Conservatively assumed SD of a 0‐100 uniform distribution
NO t ti t 0 100 l i t b ti i– NO truncation to 0‐100 scale – again to be conservative in
order to preserve the assumed SD
– Therefore, actual design performance may be even betterTherefore, actual design performance may be even better
than reported herein.
• Custom SAS program
20. Performance Characteristics Computed
1000 i l ti h f 14 tilit DRacross 1000 simulations per each of 14 utility DR curves
• Average estimated target doseg g
• Proportion of simulations in which the correct target dose was
estimated
• Proportion of simulations in which the estimated target dose
was adjacent to the correct target dose
• Average number of subjects assigned to each doseAverage number of subjects assigned to each dose
21. Results Summary
• For all 14 utility DR curves• For all 14 utility DR curves
– ≥50% of simulations yielded correct estimates of target dose
– percents ranged from 58‐98%
– median was close to 90%
– ≥91% of simulations yielded estimated target dose at or adjacent to
the true target
– Thus, the maximizing design estimates the target dose well.
•
• Most subjects were allocated at or adjacent to the true target dosej j g
– Equal allocation design would assign N=65/dose
– For all 14 utility DR curve scenarios:
• maximizing design assigned ≥68 subjects to target dose (range• maximizing design assigned ≥68 subjects to target dose (range
was 68‐105)
• range of N at dose farthest from target was 25‐62
d f d d• Hence, maximizing design is functioning as desired
22. Performance characteristics of maximizing design (N=400)
based on 1000 simulations of each utility DR curve
T E ti t d % ti ti % ti ti % ti ti t
DR#
True
Target
Dose
Estimated
Target Dose
Average
% estimating
exactly at True
Target Dose
% estimating
adjacent to True
Target Dose
% estimating at or
adjacent to True
Target Dose
1 4 3.9 93 1 95
2 1 1.3 87 4 91
3 1 1.3 77 21 98
4 1 1.1 86 14 100
5 1 1.4 58 42 100
6 2 1.6 64 36 100
7 1 1.0 96 3 99
8 2 2.3 74 25 98
9 2 1 9 72 24 969 2 1.9 72 24 96
10 4 3.9 91 9 100
11 4 3.6 59 41 100
12 3 3 0 98 2 10012 3 3.0 98 2 100
13 3 3.0 93 7 100
14 4 3.7 86 6 92
23. Average N’s assigned to each dose across 1000 simulations for
each utility DR curve
(yellow highlighted cells indicate TRUE target dose)(yellow highlighted cells indicate TRUE target dose)
Average Number of Subjects Assigned to Each Dose
DR# D1 D2 D3 D4
1 30 32 100 98
2 86 91 44 39
3 82 89 48 41
4 91 97 39 33
5 73 82 57 48
6 68 87 62 43
7 98 101 32 29
8 41 71 89 59
9 53 68 77 629 53 68 77 62
10 25 33 105 97
11 25 45 105 85
12 25 63 105 6712 25 63 105 67
13 28 64 102 66
14 31 37 99 93
24. Overall Summary
• Phase 2 trial of Test Drug versus placebo and active control• Phase 2 trial of Test Drug versus placebo and active control
• Objectives: PoC + estimate dose regimen with optimal balance between
maximum efficacy and minimum intolerance
• Maximizing adaptive dose‐finding design (Ivanova, 2009) chosen to yield g p g g ( ) y
better quality information
– fewer patients assigned to dose regimens which are ineffective or
intolerable
• True potential efficacy and tolerability dose response (DR) curves were• True potential efficacy and tolerability dose‐response (DR) curves were
constructed to span the range of potential DR curves for Test Drug
• Clinical utility function defined to combine all of the efficacy and
tolerability dose‐response curves
• Simulation study evaluated performance characteristics
• Results indicate the maximizing design
– Has high probability to estimate the correct or nearest to correct dose
with maximum clinical utility (i e “target dose”)with maximum clinical utility (i.e., target dose )
– Maximizes assignment of subjects to the target dose
– Minimizes assignment of subject to doses remote from target dose
26. Assumptions for Adaptive Design
K E d i t 0 3 i t Lik t S l Gl b l A t f R t• Key Endpoint: 0‐3 point Likert Scale Global Assessment of Response to
Therapy (0=none; 1=some; 2=good; 3=excellent)
– Since sample size is “large”, can use continuous endpoint stat methods
(i l di t ib ti )(i.e., assume normal distribution)
• Typical for global assessments of response to therapy in arthritis and
pain using Likert scale responses similar to above
– Prior data: mean difference between active & placebo 2.2 vs 1.6 (SD~0.9)
• Suggests N=41/treatment group for 80% power (alpha=0.05, 1‐sided)
• Traditional Design would have 2 or 3 dose‐combinations plus placebo (N=123
to 164)
• Investigate Adaptive Dose‐finding Phase 2 trial design with Total N=135
– 3 doses of 1st drug + 3 doses of 2nd drug (9 dose‐combinations) + placebo3 doses of 1 drug 3 doses of 2 drug (9 dose combinations) placebo
– Ivanova(2012) Bayesian Isotonic 2‐dimensional design software (CytelSim
– in‐house tool) updated to accommodate 3x3 dose‐combinations
27. Overview of Adaptive Design
I iti l C h t N 46 (10 36 l b d 4/d bi ti )• Initial Cohort N=46 (10:36, placebo:drug, 4/dose‐combination)
• 3 additional cohorts, each N=30 (7:23 pbo:drug), with doses assigned
adaptively
• Extension of Adaptive Dose‐Finding Design (Ivanova, 2012)
– Optimizes dose‐assignments for Target Responses 0.5 and 1.0 (arbitrarily
chosen, can be modified) different from placebo
– Assumes non‐decreasing response with increasing dose of each drug
within each dose‐level of the other drug
– Models the dose‐response relationship via isotonic regression
• Improves statistical efficiency compared to raw means
28. Isotonic Regression
• Nonparametric (robust) shape ExampleNonparametric (robust) shape
constrained fit (least square error
fit subject to order restriction)
• “Borrow” strength cross doses
0.4
observed proportions
isotonic fit
Example
• Typically isotonic regression
improve probability of right
selection of the target dose
0.20.3
tyofToxicity
• Better describe dose‐response
relation
• Modified for 2‐dimensions
0.00.1
Probabilit
1 2 3 4 5 6 7
0
Dose
28
29. Ivanova(2012) Bayesian Isotonic
Adaptive Dose‐Finding Design (3x3)p g g ( )
• Compute Bayesian isotonic regression means from previous
cohort(s)
– Assign patients to dose‐combinations in cohorts 2,3,4
using Bayesian posterior distribution proportions of
simulations that each dose is closest to target levels of
response
• Half the patients to lower target, half to upper target
• E g if doses 1 2 3 are closest to target dose in 25 50E.g., if doses 1,2,3 are closest to target dose in 25, 50,
and 25% of the Bayesian posterior distribution samples,
then randomization ratios are 1:2:1
• After 4 completed cohorts combine all data• After 4 completed cohorts, combine all data
– Fit isotonic regression means, test for difference from
placebo, estimate target doses, etc.
30. Assess usefulness of Adaptive Dose‐Finding Design:
Compare Performance Characteristics
Si l i l f h f h 2 i l d• Simulation results for each of the 2 potential dose‐response
scenarios are summarized by the following performance
characteristics, compared between adaptive and traditional non‐
adaptive designs:
– Power to yield statistically significant (alpha=0.05, 1‐sided)
difference from placebodifference from placebo
– Average assigned sample size per dose
– Probability of identifying the correct target doseProbability of identifying the correct target dose
• Multiple dose‐response scenarios chosen as examples of TRUE
underlying DR curves to assess performance characteristics
31. Assumed True Underlying Dose‐Response Curves
for Simulations
• “R” designates one drug
• “d” designates 2nd drug "good" response (SD=0.9)
b 1 6 d1 d2 d3• R0d0 is placebo
• R2d3 designates 2nd highest dose
of Drug R, 3rd highest dose of
pbo=1.6 d1 d2 d3
R1 1.6 2.1 2.3
R2 1.8 2.3 2.4g , g
Drug d, etc.
• Pink Highlighted Cells indicate
doses with Target Levels of
R3 2.1 2.3 2.6
"zero" response (SD=0.9)g
Response (0.5 or 1.0 different
from placebo)
zero response (SD 0.9)
pbo=1.6 d1 d2 d3
R1 1.6 1.6 1.6
R2 1 6 1 6 1 6R2 1.6 1.6 1.6
R3 1.6 1.6 1.6
35. Assumed True Underlying Dose‐Response Curves
for Simulations
• “R” designates one drug; “d” designates 2nd drug; R0d0 is placebo
• R2d3 designates middle dose (2) of Drug R, highest dose (3) of Drug d
• Pink Highlighted Cells indicate doses with Target Levels of Response (0 5 orPink Highlighted Cells indicate doses with Target Levels of Response (0.5 or
1.0 different from placebo)
3x3 format3x3 format
pbo=1.6 d1 d2 d3
R1 1.6 2.1 2.3
R2 1 8 2 3 2 4R2 1.8 2.3 2.4
R3 2.1 2.3 2.6
Linear Format R0d0 R1d1 R1d2 R1d3 R2d1 R2d2 R2d3 R3d1 R3d2 R3d3
Increasing DR 1.6 1.6 2.1 2.3 1.8 2.3 2.4 2.1 2.3 2.6g
36. Assumed True Underlying Dose‐Response Curves
for Simulations
• “R” designates one drug; “d” designates 2nd drug; R0d0 is placebo
• R2d3 designates middle dose (2) of Drug R, highest dose (3) of Drug d
• Pink Highlighted Cells indicate doses with Target Levels of Response (0 5 orPink Highlighted Cells indicate doses with Target Levels of Response (0.5 or
1.0 different from placebo)
R0d0 R1d1 R1d2 R1d3 R2d1 R2d2 R2d3 R3d1 R3d2 R3d3
increasing 1.6 1.6 2.1 2.3 1.8 2.3 2.4 2.1 2.3 2.6increasing 1.6 1.6 2.1 2.3 1.8 2.3 2.4 2.1 2.3 2.6
all2.1 1.6 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1 2.1
all2.6 1.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6 2.6
null 1 6 1 6 1 6 1 6 1 6 1 6 1 6 1 6 1 6 1 6null 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6 1.6
decreasing DR 1.6 2.6 2.35 2.1 2.35 2.1 1.85 2.1 1.85 1.6
u-shaped DR 1.6 1.6 1.85 2.1 1.85 2.6 1.85 2.1 1.85 1.6
linear DR 1.6 1.6 1.85 2.1 1.85 2.1 2.35 2.1 2.35 2.6
linear low DR 1.6 1.6 1.7 1.8 1.7 1.8 2.1 1.8 2.1 2.6
linear plateau 1.6 1.8 2.1 2.6 2.1 2.6 2.6 2.6 2.6 2.6
DR
asymmetric DR 1.6 1.85 2 2.5 1.9 2.1 2.6 1.9 2.1 2.6
Step Function 1.6 1.6 1.6 1.6 1.6 1.6 2.6 1.6 2.6 2.6
37. Number of patients assigned to each dose‐combination
Average N per Dose‐Combination Group
TRUE DR Curve R0d0 R1d1 R1d2 R1d3 R2d1 R2d2 R2d3 R3d1 R3d2 R3d3
increasing 31 10 13 13 11 11 11 12 12 13
all2.1 31 13 10 11 9 9 10 10 10 22
all2.6 31 24 11 10 11 9 9 10 9 12
null 31 8 8 10 8 8 11 9 10 33
decreasing DR 31 16 8 9 8 8 9 8 8 29decreasing DR 31 16 8 9 8 8 9 8 8 29
u-shaped DR 31 9 11 11 10 10 9 9 8 27
linear DR 31 10 11 13 10 11 12 12 12 14
linear low DR 31 9 9 12 9 11 14 11 15 16
Lin.plateauDR 31 13 14 13 13 11 9 11 9 11
asymmetricDR 31 11 11 14 10 11 11 11 13 14
With this sample size, algorithm allocates fewer patients away from doses with target
levels of response
asymmetricDR 31 11 11 14 10 11 11 11 13 14
Step Function 31 8 9 13 9 13 15 13 14 11
levels of response
In some cases, the isotonic smoothing results in increased allocation at some of the
higher dose combinations
In general, dose‐assignments are improved from equal allocation
38. Number of patients assigned to each dose‐combination
Average N per Dose‐Combination Group
TRUE DR Curve R0d0 R1d1 R1d2 R1d3 R2d1 R2d2 R2d3 R3d1 R3d2 R3d3
increasing 31 10 13 13 11 11 11 12 12 13
all2.1 31 13 10 11 9 9 10 10 10 22
all2.6 31 24 11 10 11 9 9 10 9 12
null 31 8 8 10 8 8 11 9 10 33
Only 1active2 6 31 9 9 13 13 12 13 13 10 12Only 1active2.6 31 9 9 13 13 12 13 13 10 12
Only 1active2.8 31 9 10 13 14 12 15 13 9 10
linear DR 31 10 11 13 10 11 12 12 12 14
linear low DR 31 9 9 12 9 11 14 11 15 16
Lin.plateauDR 31 13 14 13 13 11 9 11 9 11
asymmetricDR 31 11 11 14 10 11 11 11 13 14
With this sample size, algorithm allocates fewer patients away from doses with target
levels of response
asymmetricDR 31 11 11 14 10 11 11 11 13 14
Step Function 31 8 9 13 9 13 15 13 14 11
levels of response
In some cases, the isotonic smoothing results in increased allocation at some of the
higher dose combinations
In general, dose‐assignments are improved from equal allocation
39. Performance Characteristics
Power
(%)
% at
lower TGT*
%at/near
lower TGT
% at
upper TGT
% at/near
upper TGT(%) lower TGT* lower TGT upper TGT upper TGT
increasing 79 50 1 38 84
all2.1 66 19 47 92 98
all2 6 98 94 99 23 44all2.6 98 94 99 23 44
null 5.0 93 98 99 100
decreasing DR 21 24 100 04 13
h d DR 8 20 60 4 100u-shaped DR 8 20 60 4 100
linear DR 78 65 100 41 93
linear low DR 74 48 95 61 99
linear plateau DR 77 74 100 95 100
asymmetric DR 79 40 100 61 99
Step Function 66 86 100 98 100
• Relatively high power for the monotonic dose‐response configurations
p 66 86 00 98 00
* TGT = lowest dose combination with target level of response
• Moderate‐to‐high probability of estimating correct dose‐combination
• Very low probability of identifying dose‐combination NOT at or near Target
40. Performance Characteristics
Power
(%)
% at
lower TGT*
%at/near
lower TGT
% at
upper TGT
% at/near
upper TGT(%) lower TGT* lower TGT upper TGT upper TGT
increasing 79 50 1 38 84
all2.1 66 19 47 92 98
all2 6 98 94 99 23 44all2.6 98 94 99 23 44
null 5.0 93 98 99 100
Only 1active2.6 73 74 100 72 100
Only 1active2.8 72 76 100 57 100
linear DR 78 65 100 41 93
linear low DR 74 48 95 61 99
linear plateau DR 77 74 100 95 100
asymmetric DR 79 40 100 61 99
Step Function 66 86 100 98 100
• Relatively high power for the monotonic dose‐response configurations
Step Function 66 86 100 98 100
* TGT = lowest dose combination with target level of response
• Moderate‐to‐high probability of estimating correct dose‐combination
• Very low probability of identifying dose‐combination NOT at or near Target
41. Remarks & Interpretations
Ad ti D i• Adaptive Design:
– Permits assessment of more doses than traditional design
– Retains adequate power
– Tends to assign more patients towards target doses
– Has high probability of estimating “at” or “adjacent” to doses with
TRUE target levels of response
– AD permits early stopping if little or no drug effect (TBD)
42. Next Steps
• Identify design logistics (Enrolment rate, timing of end point observation, y g g ( , g p ,
cohort sizes, number of adaptations, early stopping rules, other??)
– To be addressed in draft protocol synopsis needed by end of summer
• Additional simulations to assess design improvements (after review of aboveAdditional simulations to assess design improvements (after review of above
simulation summary):
– size of initial cohort
total sample size– total sample size
– early stopping for futility
43. C S d 3Case Study 3
Adaptive Phase 2 Dose‐Finding Design
for Proof‐of‐Concept & Dose‐Exploration
via Linear Clinical Utility Function
Jim Bolognese, Cytel Inc.
EAST UGM
22Oct201422Oct2014
bolognese@cytel.com
43
44. OUTLINEOUTLINE
• Adaptive design based on linear function ofAdaptive design based on linear function of
efficacy + tolerability for clinical utility
• Simulation results of two design choices• Simulation results of two design choices
document performance characteristics of each
designdesign
44
45. Overall Summary
• Phase 2 trial of test drug versus placebog p
• Objectives: PoC + estimate dose regimen with optimal balance between
maximum efficacy and minimum intolerance
• Maximizing adaptive dose‐finding design yields better quality informationMaximizing adaptive dose finding design yields better quality information
– fewer patients assigned to dose regimens which are ineffective or
intolerable
– IVANOVA (2009)IVANOVA (2009)
– Normal Dynamic Linear Model (NDLM, COMPASS User Manual, Cytel Inc.)
• True potential efficacy and tolerability dose‐response (DR) curves were
constructed to span the range of potential DR curvesconstructed to span the range of potential DR curves
• Linear clinical utility function combines efficacy and tolerability dose‐
response curves
• Simulation study evaluated performance characteristics• Simulation study evaluated performance characteristics
• Results indicate the maximizing design
– Has high probability to estimate the correct or nearest to correct dose
ith i li i l tilit (i “t t d ”)with maximum clinical utility (i.e., “target dose”)
– Maximizes assignment of subjects at or adjacent to the target dose
– Minimizes assignment of subject to doses remote from target dose
45
46. Clinical Utility Function Definition
CU ( ffi l b ) (BP ff t l b )• CU = (efficacy vs placebo) – (BP effect vs placebo)
– Efficacy target difference from placebo = 4
– BP target difference from placebo < 10mmHg
– CU = w1*(EFF drug – EFF placebo)*(10/4)
– w2*(∆BP drug – ∆BP placebo)
• Where w1=1.5 and w2=1, i.e., Efficacy effect is weighted 50% moreWhere w1 1.5 and w2 1, i.e., Efficacy effect is weighted 50% more
than BP effect
– Examples
• 1 5*(4 – 4)*(10/4) – (10 –10) = 0• 1.5 (4 – 4) (10/4) – (10 –10) = 0
• 1.5*(4 – 4)*(10/4) – (10 –0) = ‐10
• 1.5*(4 – 0)*(10/4) – (10 –10) = +15
• 1.5*(4 – 0)*(10/4) – (10 –0) = +5
– Refer to accompanying spreadsheet for more detail and more
examples
46
49. Assumptions for Adaptive Design for Clinical Utility
5 d f d (4 8 12 16 20 ) l l b• 5 doses of test drug (4,8,12,16,20mg) plus placebo
– Total N=192
• 1st cohort N=24 (4:4:4:4:4:4)• 1 cohort N=24 (4:4:4:4:4:4)
• 10 cohorts N=14, 7 on each of 2 doses adaptively assigned
per maximizing design
– Adaptive Design permits early stopping for futility if
Conditional Power < 10% after 1st 60 patients; not considered
in initial simulationsin initial simulations
• Response Lag 2 weeks to permit 1 week observation and 1 week
for collection and analysis of data to feed adaptation
– Assumed 13/week enrolment over ~15 weeks to achieve
N=192
49
50. Results of Simulated Maximizing Adaptive Design to
Id tif D ith O ti l Cli i l UtilitIdentify Dose with Optimal Clinical Utility
• In general, the adaptive design migrates the assignment of
i dj h d i h i li i lpatients at or adjacent to the dose with maximum clinical
utility
• The design with a 2‐week lag in response works reasonablyThe design with a 2 week lag in response works reasonably
well at total N=192 for many circumstances, but there are
some clinical utility function scenarios for which it does not
(see e g scenario #12)(see, e.g., scenario #12)
• Increasing the sample size to nearly double overcomes the
deficiency in the 2‐week lag.y g
50
55. Overall Summary
• Phase 2 trial of test drug versus placebog p
• Objectives: PoC + estimate dose regimen with optimal balance between
maximum efficacy and minimum intolerance
• Maximizing adaptive dose‐finding design yields better quality informationMaximizing adaptive dose finding design yields better quality information
– fewer patients assigned to dose regimens which are ineffective or
intolerable
– IVANOVA (2009)IVANOVA (2009)
– Normal Dynamic Linear Model (NDLM, COMPASS User Manual, Cytel Inc.)
• True potential efficacy and tolerability dose‐response (DR) curves were
constructed to span the range of potential DR curvesconstructed to span the range of potential DR curves
• Linear clinical utility function combines efficacy and tolerability dose‐
response curves
• Simulation study evaluated performance characteristics• Simulation study evaluated performance characteristics
• Results indicate the maximizing design
– Has high probability to estimate the correct or nearest to correct dose
ith i li i l tilit (i “t t d ”)with maximum clinical utility (i.e., “target dose”)
– Maximizes assignment of subjects at or adjacent to the target dose
– Minimizes assignment of subject to doses remote from target dose
55