Transcript Phase II Design Strategies

Phase II Design Strategies
Sally Hunsberger
Ovarian Cancer Clinical Trials
Planning Meeting May 29, 2009
Single arm Phase II study
• Fact or fiction:
– Using single arm phase II study designs reduces
the number of patients needed in drug
development
• True: If the specified null rate is correct
• How bad can things get if the null rate is
specified incorrectly?
• Need to consider the drug development cost
(in terms of patients)
– End of phase II if don’t go to phase III
– End of phase II if go to phase III
Evaluation of designs
• Look at expected sample size E[N]
• E[N]=NII+NIII(P{continuing to phase III})
Phase II Design parameters
•
•
•
•
PFS as primary endpoint
Type I error and II error of .1
Median null PFS = 3 months
Interesting activity would result in a median
PFS of 4.5 (hazard ratio=1.5)
• Minimum follow up=3 months
• Sample size = 69
Phase III design parameters
•
•
•
•
OS as primary endpoint
Type I error 1-sided .025
Median null OS = 6 months
Interesting activity would result in a median
OS of 7.8 (hazard ratio=1.3)
• Minimum follow up = 6 months
• Sample size = 692
Under the null of no treatment benefit
• What happens if we set the null bar too low
– Go to phase III too often and this will increase the
Expected sample size,E[N].
Under null hypothesis of no Treatment effect
Null assumption is 3 months
True Median PFS
P{continuing}
E[N]
3*
.1
138
3.5
.4
346
4
.72
567
*Truth
agrees with assumption.
E[N] for a randomized phase II study is 271 with α=β=.1
Under the alternative of a treatment
benefit
• What happens if we set the null bar too high
– Do not go to phase III often enough
– This will decrease power of finding a treatment
benefit at the end of drug development
Under Alternative hypothesis of a Treatment effect
Null assumption is 3 months
Treatment benefit of a hazard ratio of 1.5
True Median PFS
P{continuing}
Probability of concluding a OS
benefit at the end of Phase III
3*
.9
.81
2.5
.59
.53
2
.1
.09
*Truth
agrees with assumption.
Probability of concluding a benefit when a randomized phase II study is used .81
If we need a randomized phase II how
can we speed up drug development
• Phase II/III design
– Futility analysis based on PFS
– Study power for a conclusion on OS
Simulation study results
• Performed simulation study so I could have
correlated PFS and OS
• Comparison designs:
– Sequence of a randomized phase II study and then
a randomized phase III
– Skip phase II go right to a phase III with a futility
analysis based on OS (appropriate if you don’t
expect an effect on PFS)
Designs
Global Null
Global Alternative
α1
t1
E[N]
E[T]
E[N]
E[T]
.2
24.0
427
28.5
649
43.2
.5
11.9
433
28.9
627
41.8
Sequence of Phase II and Phase III
.1
15.1
296
23.3
849
65.0
Integrated II/III with (f1=0)
.05
20.4
325
21.7
646
43.1
.1
16.7
294
19.6
644
42.9
.2
12.3
287
19.2
634
42.3
.5
6.1
391
26.0
625
41.7
.05
18.3
295
22.7
644
46.0
.1
14.7
268
20.9
640
45.7
.2
10.8
268
20.9
633
45.2
.5
4.2
378
28.2
623
44.5
Futility based on overall survival
Integrated II/III with (f1=3)
Over all probability of concluding a benefit when it exists is .81
Conclusions
• Single arm studies may appear to use less
patients but if the null bar is set incorrectly
this could have a major impact on E[N] and
the probability of identifying a beneficial
treatment
– When there is no true treatment effect setting the
bar two low increases the E[N]
– When there is a true treatment effect and the bar
is set too high the probability of identifying a
beneficial treatment is reduced
Conclusions
• integrated phase II/III design works well under
the global null.
– E[N] and E[T] were no larger than that of a
randomized phase II study
– E[N] and E[T] smaller than skipping the phase II study
• integrated II/III better than the separate
randomized phase II study under the global
alternative
– did not increase E[T] and E[N] when compared to
skipping the phase II component.