exposure-ae-dropout analysis in patients treated with pregabalin. raymond miller pfizer global...
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Exposure-AE-Dropout Analysis in Patients treated with
pregabalin.
Raymond Miller
Pfizer Global Research and Development
Issue
• A new 2 ligand (PD0332334) that has anxiolytic properties was in development.
• Little was known about AE’s for this compound, however, extensive knowledge from other 2 ligands (pregabalin) available.
• It is generally believed that dose titration may reduce AE’s.
Objective
• To characterize the relationship between PD0332334 dose, patient characteristics, time, severity and frequency of dizziness and somnolence in patients with GAD.
Questions
• Would AE frequency be different if the drug was titrated to the target dose?
• How long do we need to titrate to minimize AE’s?
• How many dose steps do we need to minimize AE’s?
Current Information
• Multiple phase 3 trials with pregabalin titrated over 3 to 7 days to attain steady state dose in the treatment of GAD.
• One phase 4 study with three treatment groups: placebo, pregabalin 600 mg/day fixed, 150-600 mg/day titrated..
GlobalPharmacometrics
Phase 3 trialsGAD patients treated with Pregabalin
1630 patient’s information (47218 observations) was pooled from 6 clinical studies.
All studies consisted of treatment arms with a dose titration phase varying from 3 to 7 days followed by a three or five week maintenance.
Dizziness was spontaneously recorded using a daily diary as none=0, mild=1, moderate=2, and severe=3.
Dropout was recorded as such up to 3 days before scheduled conclusion of the study.
Objectives
• To describe the exposure-longitudinal AE severity relationship following multiple doses of pregabalin.
• To describe the relationship between AE and patient dropout
• To explore the relationship between dose titration of pregabalin and dropout
Frequency of dizziness by day and dose
GlobalPharmacometrics
Exposure-Dizziness-Dropout in GAD patients treated with Pregabalin
Models were developed for exposure-AE as well as AE-dropout.
For AE separate models were developed for the incidence of adverse event and for the conditional severity of adverse event given that an adverse event has occurred.
The unconditional severity probability distribution was obtained by summing the joint probabilities.
Dropout was modeled using a discrete time survival model.
0 4 7 11 15 19 23 0 4 7 11 15 19 23
0 4 7 11 15 19 23
ETA
0.0
0.1
0.2
0.3
0.40.0
0.1
0.2
0.3
0.40.0
0.1
0.2
0.3
0.4P
ropo
rtio
n
DOSE: 0 DOSE: 50 DOSE: 75
DOSE: 150 DOSE: 200 DOSE: 300
DOSE: 400 DOSE: 450 DOSE: 600
Histogram of ETA's - Dizziness
Assumption that j ~ Niid(0, 2) is violated.
Incidence Model
The probability of incidence of dizziness was modeled using a nonlinear logistic regression model given by the expression:
The incidence model does not contain an inter-individual random effect because AEi is observed only once for each patient
Sigmoid Emax model best describes the drug effect although γ is not well estimated
d
i
ii f
p
pAEP
1
log1g
Placebo 150 200 300 400 450 600
02
04
06
08
01
00
Observed
Inc
ide
nc
e o
f AE
(D
izz
ine
ss
) (%
)
Placebo 150 200 300 400 450 600
02
04
06
08
01
00
Predicted
Inc
ide
nc
e o
f AE
(D
izz
ine
ss
) (%
)
NoYes
35.3 (33.0-38.7)33.8600
35.3 (32.9-38.7)38.2450
35.2 (32.9-38.5)35.7400
35.0 (31.7-38.2)35.2300
31.3 (22.7-37.3)33.8200
14.2 (9.6-19.1)13.8150
8.2 (5.8-10.9)8.5Placebo
Predicted (Mean and 95%CI*)
Observed
Dizziness Incidence (%)Daily Dose(mg/day)
* obtained from non-parametric bootstrap (n=1000)
Placebo 150 200 300 400 450 600
02
04
06
08
01
00
Observed
Inc
ide
nc
e o
f AE
(D
izz
ine
ss
) (%
)
Placebo 150 200 300 400 450 600
02
04
06
08
01
00
Predicted
Inc
ide
nc
e o
f AE
(D
izz
ine
ss
) (%
)
NoYes
35.3 (33.0-38.7)33.8600
35.3 (32.9-38.7)38.2450
35.2 (32.9-38.5)35.7400
35.0 (31.7-38.2)35.2300
31.3 (22.7-37.3)33.8200
14.2 (9.6-19.1)13.8150
8.2 (5.8-10.9)8.5Placebo
Predicted (Mean and 95%CI*)
Observed
Dizziness Incidence (%)Daily Dose(mg/day)
* obtained from non-parametric bootstrap (n=1000)
35.3 (33.0-38.7)33.8600
35.3 (32.9-38.7)38.2450
35.2 (32.9-38.5)35.7400
35.0 (31.7-38.2)35.2300
31.3 (22.7-37.3)33.8200
14.2 (9.6-19.1)13.8150
8.2 (5.8-10.9)8.5Placebo
Predicted (Mean and 95%CI*)
Observed
Dizziness Incidence (%)Daily Dose(mg/day)
* obtained from non-parametric bootstrap (n=1000)
Observed vs. PredictedIncidence Model
Conditional Severity Model
The probability of each severity (none, mild, moderate, severe) was modeled with a proportional odds model. The conditional severity model given by the expression:
Drug exposure was based on the intended daily dose (titrated) of pregabalin.
Emax model with time-course placebo effect and a component with an exponential attenuation best describe the AE severity.
m
kidpkiiij ffAEmYPg
1
,1|
Dataset and NONMEM control stream$PRED B1=THETA(1) B2=B1+THETA(2) B3=B2+THETA(3)
;logits for Y>=1, Y>=2, Y.=3RESP=0A1 = B1 + RESP + ETA(1)A2 = B2 + RESP + ETA(1)A3 = B3 + RESP + ETA(1)
C1=EXP(A1) C2=EXP(A2) C3=EXP(A3)
;probabilities for Y>=1, Y>=2, Y>=3 P1=C1/(1+C1) P2=C2/(1+C2) P3=C3/(1+C3)
;Probabilities for Y=0 Y=1, Y=2, Y=3 PA=1-P1 PB=P1-P2 PC=P2-P3 PD=P3
SID TIME DOSE AE11 1 50 01 2 50 01 3 100 01 4 100 11 5 100 11 6 150 11 7 150 21 8 150 21 9 150 11 10 150 11 11 150 01 12 150 01 13 150 01 14 150 02 1 100 02 2 100 02 3 200 12 4 200 12 5 400 12 6 400 12 7 600 12 8 600 12 9 600 12 10 600 12 11 600 12 12 600 02 13 600 02 14 600 0
… … … …
Observed vs. PredictedConditional Severity Model
Dose: 0 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 150 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 200 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 300 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 400 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 450 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 600 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Pobs (Y>=1)Pobs (Y>=2)Pobs (Y>=3)Ppred (Y>=1)Ppred (Y>=2)Ppred (Y>=3)
Markov Model
Markov elements have been incorporated to account for the correlation between neighboring observations within a subject:
The logistic function (proportional odds model) and the same structures obtained with the conditional severity model was used.
m
kihYdphkiijiij ij
ffhYAEmYPg1
,,1 1),,1|(
Dataset and NONMEM control stream$PRED B1=THETA(1) B2=B1+THETA(2) B3=B2+THETA(3)
IF(PRE1.EQ.1) THEN B1=THETA(4) B2=B1+THETA(5) B3=B2+THETA(6)ENDIF
IF(PRE1.EQ.2) THEN B1=THETA(7) B2=B1+THETA(8) B3=B2+THETA(9)ENDIF
IF(PRE1.EQ.3) THEN B1=THETA(10) B2=B1+THETA(11) B3=B2+THETA(12)ENDIF
RESP=0 A1 = B1 + RESP + ETA(1) A2 = B2 + RESP + ETA(1) A3 = B3 + RESP + ETA(1).. ..
SID TIME DOSE AE1 PRE11 1 50 0 01 2 50 0 01 3 100 0 01 4 100 1 01 5 100 1 11 6 150 1 11 7 150 2 11 8 150 2 21 9 150 1 21 10 150 1 11 11 150 0 11 12 150 0 01 13 150 0 01 14 150 0 02 1 100 0 02 2 100 0 02 3 200 1 02 4 200 1 12 5 400 1 12 6 400 1 12 7 600 1 12 8 600 1 12 9 600 1 12 10 600 1 12 11 600 1 12 12 600 0 12 13 600 0 02 14 600 0 0
… … … … …
Observed vs. PredictedConditional Severity Model with Markov
Dose: 0 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 150 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 200 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 300 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 400 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 450 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Dose: 600 -mg/day
Days
P(Y
j>=
m|A
E=
1)
0 10 20 30 40
0.0
0.4
0.8
Pobs (Y>=1)Pobs (Y>=2)Pobs (Y>=3)Ppred (Y>=1)Ppred (Y>=2)Ppred (Y>=3)
Simulation Step(example: Time-course of incidence)
Dose: 0 -mg/day
Days
Pr(A
E1)
0 10 20 30 40
0.0
0.1
0.2
0.3
0.4
Dose: 150 -mg/day
Days
Pr(A
E1)
0 10 20 30 40
0.0
0.1
0.2
0.3
0.4
Dose: 200 -mg/day
Days
Pr(A
E1)
0 10 20 30 40
0.0
0.1
0.2
0.3
0.4
Dose: 300 -mg/day
Days
Pr(A
E1)
0 10 20 30 40
0.0
0.1
0.2
0.3
0.4
Dose: 400 -mg/day
Days
Pr(A
E1)
0 10 20 30 40
0.0
0.1
0.2
0.3
0.4
Dose: 450 -mg/day
Days
Pr(A
E1)
0 10 20 30 40
0.0
0.1
0.2
0.3
0.4
Dose: 600 -mg/day
Days
Pr(A
E1)
0 10 20 30 40
0.0
0.1
0.2
0.3
0.4
Day
Pro
b(E
ven
t)
0 10 20 30 40
0.0
0.1
00
.20
0.3
0
Dose: 0 mg/day
Day
Pro
b(E
ven
t)
0 10 20 30 40
0.0
0.1
00
.20
0.3
0
Dose: 150 mg/day
Day
Pro
b(E
ven
t)
0 10 20 30 40
0.0
0.1
00
.20
0.3
0
Dose: 200 mg/day
Day
Pro
b(E
ven
t)
0 10 20 30 40
0.0
0.1
00
.20
0.3
0
Dose: 300 mg/day
Day
Pro
b(E
ven
t)
0 10 20 30 40
0.0
0.1
00
.20
0.3
0
Dose: 400 mg/day
Day
Pro
b(E
ven
t)
0 10 20 30 40
0.0
0.1
00
.20
0.3
0
Dose: 450 mg/day
Day
Pro
b(E
ven
t)
0 10 20 30 40
0.0
0.1
00
.20
0.3
0
Dose: 600 mg/day
Observed vs Simulated Incidenceof Adverse Event by Dose
(N = 180 Simulations)
Probability of IncidenceID=1
ID=2
ID=3
ID=1630
……
..
N times simulations
“Mean of trial”
“Summary of Mean”
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30 40
DAY
AE
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30 40
DAY
AE
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30 40
DAY
AE
0.0
0.2
0.4
0.6
0.8
1.0
0 10 20 30 40
DAY
AE
Original Dataset
Posterior Predictive Check Distributions of the Number of the Different
Transitions
3000 5000 7000
010
2030
t ransit count
0 -> 0
500 1000 1500 2000
010
2030
40
t ransit count
0 -> 1
200 400 600 800
010
2030
40
t ransit count
0 -> 2
20 40 60 80 100
010
2030
40
t ransit count
0 -> 3
500 1000 1500 2000
010
2030
40
t ransit count
1 -> 0
1000 2000 3000
010
2030
4050
t ransit count
1 -> 1
0 200 400 600 800
010
2030
t ransit count
1 -> 2
0 20 40 60
010
2030
t ransit count
1 -> 3
200 400 600 800
010
2030
40
t ransit count
2 -> 0
0 200 400 600 800
010
2030
t ransit count
2 -> 1
400 600 800 1000
010
2030
t ransit count
2 -> 2
0 10 20 30 40 50 60
010
2030
4050
t ransit count
2 -> 3
20 40 60 80 100
010
2030
t ransit count
3 -> 0
0 20 40 60
010
2030
40
t ransit count
3 -> 1
0 10 20 30 40 50 60
05
1015
2025
30
t ransit count
3 -> 2
0 50 100 150 200
010
2030
40
t ransit count
3 -> 3
6000 6400 6800 7200
05
1015
2025
30
t ransit count
0 -> 0
220 240 260 280
010
2030
t ransit count
0 -> 1
140 160 180 200
05
1015
2025
30
t ransit count
0 -> 2
15 20 25 30 35 40
010
2030
40
t ransit count
0 -> 3
150 170 190 210
010
2030
40
t ransit count
1 -> 0
2200 2600 3000 3400
010
2030
40
t ransit count
1 -> 1
0 2 4 6 8 10
010
2030
40
t ransit count
1 -> 2
-1.0 0. 0 0. 5 1. 0
020
4060
8010
0
t ransit count
1 -> 3
80 100 120 140 160
010
2030
t ransit count
2 -> 0
5 10 15 20 25
010
2030
4050
60
t ransit count
2 -> 1
1000 1400 1800
010
2030
40
t ransit count
2 -> 2
0 1 2 3 4
020
4060
t ransit count
2 -> 3
5 10 15 20 25 30
010
2030
40
t ransit count
3 -> 0
-1.0 0. 0 0. 5 1. 0
020
4060
8010
0
t ransit count
3 -> 1
0 1 2 3 4 5 6
010
2030
4050
t ransit count
3 -> 2
100 200 300 400 500
05
1015
2025
30
t ransit count
3 -> 3
3000 5000 7000
010
2030
t ransit count
0 -> 0
500 1000 1500 2000
010
2030
40
t ransit count
0 -> 1
200 400 600 800
010
2030
40
t ransit count
0 -> 2
20 40 60 80 100
010
2030
40
t ransit count
0 -> 3
500 1000 1500 2000
010
2030
40
t ransit count
1 -> 0
1000 2000 3000
010
2030
4050
t ransit count
1 -> 1
0 200 400 600 800
010
2030
t ransit count
1 -> 2
0 20 40 60
010
2030
t ransit count
1 -> 3
200 400 600 800
010
2030
40
t ransit count
2 -> 0
0 200 400 600 800
010
2030
t ransit count
2 -> 1
400 600 800 1000
010
2030
t ransit count
2 -> 2
0 10 20 30 40 50 60
010
2030
4050
t ransit count
2 -> 3
20 40 60 80 100
010
2030
t ransit count
3 -> 0
0 20 40 60
010
2030
40
t ransit count
3 -> 1
0 10 20 30 40 50 60
05
1015
2025
30
t ransit count
3 -> 2
0 50 100 150 200
010
2030
40
t ransit count
3 -> 3
6000 6400 6800 7200
05
1015
2025
30
t ransit count
0 -> 0
220 240 260 280
010
2030
t ransit count
0 -> 1
140 160 180 200
05
1015
2025
30
t ransit count
0 -> 2
15 20 25 30 35 40
010
2030
40
t ransit count
0 -> 3
150 170 190 210
010
2030
40
t ransit count
1 -> 0
2200 2600 3000 3400
010
2030
40
t ransit count
1 -> 1
0 2 4 6 8 10
010
2030
40
t ransit count
1 -> 2
-1.0 0. 0 0. 5 1. 0
020
4060
8010
0
t ransit count
1 -> 3
80 100 120 140 160
010
2030
t ransit count
2 -> 0
5 10 15 20 25
010
2030
4050
60
t ransit count
2 -> 1
1000 1400 1800
010
2030
40
t ransit count
2 -> 2
0 1 2 3 4
020
4060
t ransit count
2 -> 3
5 10 15 20 25 30
010
2030
40
t ransit count
3 -> 0
-1.0 0. 0 0. 5 1. 0
020
4060
8010
0
t ransit count
3 -> 1
0 1 2 3 4 5 6
010
2030
4050
t ransit count
3 -> 2
100 200 300 400 500
05
1015
2025
30
t ransit count
3 -> 3
without Markov with Markov
The vertical line in each plot represents the observed number of transition in the original dataset
Simulation (≥mild)Severity Model with Markov
Dose: 0 -mg/day
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 150 -mg/day
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 200 -mg/day
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 300 -mg/day
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 400 -mg/day
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 450 -mg/day
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 600 -mg/day
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.1
00
.25
Simulated Probabilities Are Presented By Means (lines) with 95% CI (dash lines) and 80 %CI (shades) from 100 Simulations.
Simulation (≥ moderate)Severity Model with Markov
Dose: 0 -mg/day
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 150 -mg/day
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 200 -mg/day
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 300 -mg/day
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 400 -mg/day
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 450 -mg/day
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 600 -mg/day
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.1
00
.25
Simulated Probabilities Are Presented By Means (lines) with 95% CI (dash lines) and 80 %CI (shades) from 100 Simulations.
Simulation (≥severe)Severity Model with Markov
Dose: 0 -mg/day
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 150 -mg/day
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 200 -mg/day
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 300 -mg/day
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 400 -mg/day
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 450 -mg/day
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.1
00
.25
Dose: 600 -mg/day
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.1
00
.25
Simulated Probabilities Are Presented By Means (lines) with 95% CI (dash lines) and 80 %CI (shades) from 100 Simulations.
GlobalPharmacometrics
Conclusion The probability of experiencing dizziness during any day increases
with pregabalin daily dose.
The predicted mean incidence of dizziness was around 35 % at daily dose of 200 mg/day or greater, which was at least 2 fold higher compared to those of at daily doses <150 mg/day.
The most frequently reported severity was mild to moderate. The risk of experience dizziness with any severity increases within 1 week, but decline to over the next 3 to 4 weeks. The risk of mild or moderate dizziness increases up to 25 % within 1 week, and declines to around 7 % over 3 to 4 weeks.
The proportional odds model including a Markov element could describe the time-course of probability of dizziness well.
Dropout Model
Dropout Across Doses
Days
Pro
ba
bili
ty o
f R
em
ain
ing
in S
tud
y
0 10 20 30 40
0.6
0.7
0.8
0.9
1.0
405 344 319 303 29290 83 81 73 73
185 171 163 157 14891 91 85 82 8277 74 65 63 57
210 192 182 172 172484 439 401 372 364
600mg450mg400mg300mg200mg150mgPlacebo
Dropout Model
• Dropout was modeled using a discrete time survival model (Gompertz).
• Dizziness severity was included in the model as a covariate.
t
t
tt dwYwgtS
tSYtTtTtP
1
11 ),(exp11
1,11
g(w,Yt-1) represents the hazard function
Simulations of dropout probabilities based on simulated severity of dizziness stratified by representative unique dose titration profiles over time. Observed (red line)
TIME (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in S
tud
y
0 10 20 30 40
0.5
0.7
0.9
Titration Scheme 2 DOSE=0 - STUDY=85&87
TIME (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in S
tud
y
0 10 20 30 40
0.5
0.7
0.9
Titration Scheme 3 DOSE=150 - STUDY=21&25&26
TIME (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in S
tud
y
0 10 20 30 40
0.5
0.7
0.9
Titration Scheme 4 DOSE=200 - STUDY=85
TIME (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in S
tud
y
0 10 20 30 40
0.5
0.7
0.9
Titration Scheme 6 DOSE=400 - STUDY=85
TIME (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in S
tud
y
0 10 20 30 40
0.5
0.7
0.9
Titration Scheme 7 DOSE=400 - STUDY=87
TIME (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in S
tud
y
0 10 20 30 40
0.5
0.7
0.9
Titration Scheme 8 DOSE=450 - STUDY=83
TIME (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in S
tud
y
0 10 20 30 40
0.5
0.7
0.9
Titration Scheme 9 DOSE=450 - STUDY=85
TIME (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in S
tud
y
0 10 20 30 40
0.5
0.7
0.9
Titration Scheme 11 DOSE=600 - STUDY=83
TIME (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in S
tud
y0 10 20 30 40
0.5
0.7
0.9
Titration Scheme 12 DOSE=600 - STUDY=87
Time (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in th
e S
tud
y
0 10 20 30 40
0.5
0.7
0.9
No Dizziness (AE1=0)
Time (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in th
e S
tud
y
0 10 20 30 40
0.5
0.7
0.9
Mild Dizziness (AE=1)
Time (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in th
e S
tud
y
0 10 20 30 40
0.0
0.4
0.8
Moderate Dizziness (AE=2)
Time (Days)
Pro
ba
bili
ty o
f R
em
ain
ing
in th
e S
tud
y
0 10 20 30 40
0.0
0.4
0.8
Severe Dizziness (AE=3)
GOF 5th – 95th prediction interval constructed from 200 simulations using the original dataset structure as well as median model predicted dropout (grey
line) and Kaplan-Meier estimates of in study-survival (black line).
External Validation:Pregabalin BID Add-On Titration Trial: A Randomized, Double-Blind,Placebo-Controlled, Parallel-Group, Multicenter Study in Patients With Partial Seizures (1008-157)
TIME TO WITHDRAWAL
External Validation:Observed (Kaplan Meier) dropout from an independent 12 week GAD trial (red line) with either placebo or 600 mg daily pregabalin treatment and its corresponding 5th-95th nonparametric confidence intervals at weekly increments. Gray polygon outlines a prediction interval of 5th and 95th quantiles of 1000 trial simulation using the described GAD dropout model
Titration Scenario’s300 mg daily ITT
# Scenario 1 (1week): 50x2, 100, 150, 200, 250, 300
# Scenario 2 (2week): 50x3, 100x3, 150x2, 200x2, 250x2, 300...
# Scenario 3 (3week): 50x4, 100x4, 150x4, 200x4, 250x3, 300...
# Scenario 4 (4week): 50x6, 100x5, 150x5, 200x5, 250x5, 300...
# Scenario 5 (6week): 50x8, 100x8, 150x8, 200x8, 250x8, 300...
>=mildScenario: 1
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5Scenario: 2
Days
P(Y
s>=
mild
)0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 3
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 4
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 5
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Maximum (Peak) Mean Probability for Each Scenario1 week titration--Scenario 1 (0.22)2 week titration--Scenario 2 (0.196)3 week titration--Scenario 3 (0.193)4 week titration--Scenario 4 (0.182)6 week titration--Scenario 5 (0.176)
>=moderateScenario: 1
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5Scenario: 2
Days
P(Y
s>=
mo
de
rate
)0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 3
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 4
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 5
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Maximum (Peak) Mean Probability for Each Scenario1 week titration--Scenario 1 (0.105)2 week titration--Scenario 2 (0.086)3 week titration--Scenario 3 (0.084)4 week titration--Scenario 4 (0.075)6 week titration--Scenario 5 (0.071)
>=severe
Note: y-axis scale is adjusted to enlarge the AE profile
Scenario: 1
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.0
10
.02
0.0
30
.04
0.0
5Scenario: 2
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.0
10
.02
0.0
30
.04
0.0
5
Scenario: 3
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.0
10
.02
0.0
30
.04
0.0
5
Scenario: 4
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.0
10
.02
0.0
30
.04
0.0
5
Scenario: 5
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.0
10
.02
0.0
30
.04
0.0
5
Maximum (Peak) Mean Probability for Each Scenario
1 week titration--Scenario 1 (0.00922)2 week titration--Scenario 2 (0.00638)3 week titration--Scenario 3 (0.00601)4 week titration--Scenario 4 (0.005)6 week titration--Scenario 5 (0.00456)
Simulated GAD survival probabilities from the combined Dizziness-dropout model. Two dosing schemes (blue) within a weeklong titration regimen differ only over 3 initial days of dosing.
Days in Study
Pro
ba
bili
ty o
f Re
ma
inin
g in
Stu
dy
0 7 14 21 28 35 42
0.6
0.7
0.8
0.9
1.0
10000 9382 8727 8081 7511 6986 648410000 9195 8451 7785 7211 6696 620910000 9123 8369 7695 7130 6610 6112
Placebo300x3-450x3-600 mg600 mg
Days in Study
Pro
ba
bili
ty o
f Re
ma
inin
g in
Stu
dy
0 7 14 21 28 35 42
0.6
0.7
0.8
0.9
1.0
10000 9382 8727 8081 7511 6986 648410000 9281 8576 7927 7369 6850 635810000 9123 8369 7695 7130 6610 6112
Placebo150x3-450x3-600mg600 mg
Next Steps
• Clinical Trial Simulations using different titration scenarios.– Titration over different times– Variations in the first week. – Scaling to drugs in same class
Acknowledgements
• Kaori Ito
• Bojan Lalovic
• Matt Hutmacher
Backup
>=mildScenario: 1
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5Scenario: 2
DaysP
(Ys>
=m
ild)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 3
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 4
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 5
Days
P(Y
s>=
mild
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Maximum (Peak) Mean Probability for Each Scenario1 week titration--Scenario 1 (0.235)2 week titration--Scenario 2 (0.208)3 week titration--Scenario 3 (0.196)4 week titration--Scenario 4 (0.194)6 week titration--Scenario 5 (0.182)
>=moderateScenario: 1
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5Scenario: 2
DaysP
(Ys>
=m
od
era
te)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 3
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 4
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Scenario: 5
Days
P(Y
s>=
mo
de
rate
)
0 10 20 30 40
0.0
0.0
50
.15
0.2
5
Maximum (Peak) Mean Probability for Each Scenario1 week titration--Scenario 1 (0.119)2 week titration--Scenario 2 (0.094)3 week titration--Scenario 3 (0.085)4 week titration--Scenario 4 (0.083)6 week titration--Scenario 5 (0.076)
>=severe
Note: y-axis scale is adjusted to enlarge the AE profile
Scenario: 1
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.0
10
.02
0.0
30
.04
0.0
5Scenario: 2
DaysP
(Ys>
=se
vere
)0 10 20 30 40
0.0
0.0
10
.02
0.0
30
.04
0.0
5
Scenario: 3
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.0
10
.02
0.0
30
.04
0.0
5
Scenario: 4
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.0
10
.02
0.0
30
.04
0.0
5
Scenario: 5
Days
P(Y
s>=
seve
re)
0 10 20 30 40
0.0
0.0
10
.02
0.0
30
.04
0.0
5
Maximum (Peak) Mean Probability for Each Scenario
1 week titration--Scenario 1 (0.01135)2 week titration--Scenario 2 (0.00701)3 week titration--Scenario 3 (0.00635)4 week titration--Scenario 4 (0.00578)6 week titration--Scenario 5 (0.00513)
Day
Prob
abilit
y
1 7 14 21 28 35 42
0.70
0.75
0.80
0.85
0.90
0.95
1.00
TTR0
0.94 0.888 0.84 0.796 0.762 0.735
TTR1
0.933 0.88 0.83 0.79 0.755 0.726
TTR2
0.933 0.874 0.825 0.786 0.749 0.723
TTR3
0.936 0.883 0.838 0.797 0.76 0.733
TTR4
0.933 0.88 0.831 0.789 0.753 0.725
TTR5
0.94 0.884 0.837 0.796 0.761 0.735
TTR6
0.923 0.867 0.821 0.782 0.746 0.719
TTR0-Placebo
TTR1-1 week Titration 50 50 100 150 200 250 300TTR2-2 weeks Titration 50 50 50 100 100 100 150 150 200 200 250 250 300TTR3-3 weeks Titration 50 50 50 50 100 100 100 100 150 150 150 150 200 200 200 200 250 250 250 300TTR4-4 weeks Titration 50 50 50 50 50 50 100 100 100 100 100 150 150 150 150 150 200 200 200 200 200 250 250 250 250 250 300TTR5-5 weeks Titration 50 50 50 50 50 50 50 50 100 100 100 100 100 100 100 100 150 150 150 150 150 150 150 150 200 200 200 200 200 200 200 200 250 250 250 250 250 250 250 250 300
TTR6-No titration 300 6 weeks
Simulation of dropout
Day
Pro
babi
lity
1 7 14 21 28 35 42
0.70
0.75
0.80
0.85
0.90
0.95
1.00
TTR0
0.936 0.882 0.836 0.795 0.759 0.733
TTR6
0.919 0.864 0.819 0.782 0.748 0.723
Day
Pro
babi
lity
1 7 14 21 28 35 42
0.70
0.75
0.80
0.85
0.90
0.95
1.00
TTR0
0.936 0.882 0.836 0.795 0.759 0.733
TTR1
0.924 0.871 0.824 0.782 0.744 0.718
TTR6
0.919 0.864 0.819 0.782 0.748 0.723
Day
Pro
babi
lity
1 7 14 21 28 35 42
0.70
0.75
0.80
0.85
0.90
0.95
1.00
TTR0
0.936 0.882 0.836 0.795 0.759 0.733
TTR3
0.935 0.88 0.833 0.791 0.753 0.726
TTR6
0.919 0.864 0.819 0.782 0.748 0.723
Day
Pro
babi
lity
1 7 14 21 28 35 42
0.70
0.75
0.80
0.85
0.90
0.95
1.00
TTR0
0.936 0.882 0.836 0.795 0.759 0.733
TTR5
0.937 0.883 0.838 0.794 0.758 0.73
TTR6
0.919 0.864 0.819 0.782 0.748 0.723
Graphical Data Exploration- Nonparametric/Kaplan Meier Analysis Poolability of Placebo Cohorts GAD
Days
Pro
ba
bili
ty o
f R
em
ain
ing
in
Stu
dy
0 10 20 30 40
0.6
0.7
0.8
0.9
1.0
101 98 93 91 8586 81 71 66 6491 80 74 67 6767 60 55 49 4970 66 59 55 5569 60 54 51 51
484 439 401 372 364
Study87Study85Study83Study26Study25Study21Median(Pooled)
In Splus (survfit) only accommodates categorical time-invariant covariates (strata)!
At ti there are di events
(dropouts) and ni
individuals (“at risk”).
number of events
number at risk
tt i
i
i n
d)t(S 1
Days
Surv
ival
Pro
babi
lity
0 10 20 30 40 50
0.65
0.70
0.75
0.80
0.85
0.90
0.95
1.00
97 93 90 89 8088 80 74 70 65
484 439 401 155 139400mg-S87400-S85Placebo
Comparison of Dropout Across Titration Schemes GAD
Days
Prob
abilit
y of R
emain
ing in
Stud
y
0 10 20 30 40
0.60.7
0.80.9
1.0
109 95 91 91 8289 81 7366 55 5071 60 5270 61 56
484 439 401 155 139
600mg-S87600mg-S83600mg-S26600mg-S25600mg-S21Placebo
600 mg400 mg
Longer titration (time-to-attainment of randomized dose) ->lower dropout
Study 87- an outlier?
RD@ Day 6 +100 mg/day
RD@ Day 7 +150 mg/day