determine if a new agent or a new treatment regimen appears sufficiently efficacious to be worth...

Some Considerations for Choosing Among Types of

Phase II Designs

Paul Catalano

June 26, 2009

Determine if a new agent or a new treatment regimen appears sufficiently efficacious to be worth further investigation◦ Not attempting to prove or establish that the new

agent improves outcome Verify the safety of the therapy Provide statistical rigor/formal evaluation

context and targeted patient population

Purpose of Phase II Studies

Often formulate as testing a null hypothesis vs. an alternative◦ E.g. H0: pr = 0.05 vs. Ha: pr = 0.20, where pr is the

true proportion of patients who will respond to the new agent

Consequence of a type I error (): an ineffective agent will be studied further ◦ Use = 0.10 (one-sided)◦ Larger than in phase III studies

General Approach

Consequence of a type II error (): an effective agent will not be studied further◦ should be < 0.10

In practice, tend to be multiple phase II studies performed in multiple diseases, so the overall chance of missing an effective treatment is lower

Selection of therapies for phase III testing is based on all available data, not on a single phase II study

General Approach

Single arm with single analysis (can have multiple single arm studies in one protocol)

Single arm with interim stopping rules (usually with suspension of accrual)

Randomized selection designs(pick-the-winner)

Comparative randomized control Randomized discontinuation designs

Types of Phase II Designs

Patients refractory to standard therapy If some patients improve, agent must have

some activity Often use H0: pr = 0.05 vs. Ha: pr = 0.20 Simon’s (1989) optimal two-stage designs

minimize expected sample size under H0

Classic Design for Screening New Agents

Simon’s optimal design for pr = 0.05 vs 0.20:◦ 1st stage: treat 12 patients; stop if no responses◦ 2nd stage: treat 25 patients; conclude inactive if

< 4 / 37 (11%) respond

CTEP / IDB has been pushing this design for new agents in diseases without prior evidence of activity


Single arm two-stage designs are inefficient for multicenter studies◦ Time and effort needed to develop protocol and

CRFs and set up database◦ Cost of activation at institutions

Prefer settings where single stage designs are appropriate or studies with multiple strata and / or multiple arms


Might be appropriate◦ If some prior evidence of activity ◦ For combinations of new drugs with standard

treatments

Example: H0: pr = 0.20 vs Ha: pr = 0.37 (null rate depends on level of activity for standard rx)◦ 1-stage: 45 patients, reject H0 if > 12 / 45 (27%)

respond◦ 2-stage: conclude inactive if < 5 / 25 (20% 1st

stage) or 13/50 (26% overall) respond

Single Stage Accrual Designs

Cytostatic agents might improve disease stabilization rates rather than improve response rates

Test for improvement in disease stabilization rates; e.g. H0: ps = 0.30 vs. Ha: ps = 0.50, where ps = proportion stable or responding (free of progression) at x months (e.g. x = 4)

Calculations the same as for response

Improvement in Disease Stabilization

Multinomial: test e.g. H0: pr = 0.05 and ps = 0.30 vs. Ha: pr > 0.05 or ps > 0.30◦ Less efficient than binomial◦ May be more difficult to interpret

TTP or PFS◦ Kaplan-Meier estimate at single time or other

nonparametric test◦ Parametric (e.g. exponential) models can be

slightly more efficient

Survival generally not appropriate

Other Endpoints

Test e.g. H0: pr = 0.05 and ps = 0.30 vs. Ha: pr > 0.05 or ps > 0.30

Need to consider power against multiple alternative values; e.g. Ha1: pr = 0.20, ps = 0.30Ha2: pr = 0.14, ps = 0.40Ha3: pr = 0.05, ps = 0.50

1-stage: n=46, reject H0 if > 6 responses or >20 cases responding or stable◦ = 0.09; power = 0.92 for Ha1, Ha2, & Ha3

Example Multinomial Design

Separate evaluation of each arm◦ Each arm evaluated in a similar population

Selection designs: select the ‘best’ arm for further study

Comparative randomized control Randomized discontinuation

Randomized designs are larger and more complex – need to explain each arm to patients

Types of Randomized Phase II Designs

Concern about selection bias in studies without a simultaneous control group◦ Studies can enroll different patient groups even

with the same nominal population

◦ Population drift and stage migration

Control groups more appropriate for evaluating contribution to a combination or effect on progression than for determining if any response activity

Comparing studies from different groups

Control Arms

Often not needed because◦ Phase II studies can only detect fairly large

effects, so biases would need to be large

◦ Consequence of a false positive is further testing of an inactive drug

◦ Cooperative group or other studies conducted in the same network with central data review produce fairly consistent results

Increase the time and expense for phase II evaluation

Control Arms

(Simon, Wittes and Ellenberg, 1985) randomize between 2 or more experimental arms (no control arm)◦ In a sense, least efficacious arm is a control for

the others Select the best arm for further evaluation Usually define ‘best’ to be the arm with the

best outcome, no matter how small the difference

Randomized Selection (Pick-the-Winner) Design

With two arms, 0.50◦ Rationale: doesn’t matter which arm is

selected if they are nearly equivalent

Often separate efficacy test for each arm, too◦ 1-stage or 2-stage

Usually prefer randomizing over a series of separate studies◦ Facilitates (informal) comparisons

◦ Guards against sampling bias



RANDOMIZE

RX1

RX2

RXk. . .

Estimated Resp Rate

R1/n1

R2/n2

Rk/nk

. . .

RXj is ‘best’ if Rj/nj > Ri/ni for i j

Can use other endpoints

Example: Simon’s optimal 2-stage design for H0: pr = 0.20 vs Ha: pr = 0.40 enrolls 17 patients in the 1st stage and 20 in the 2nd ( = = .10)

Apply this design to each arm in a 2-arm randomized selection design


Prob arm is winner

pr1 pr2 RX1 RX2 Neither

.20 .40 .015 .890 .095

.30 .40 .147 .758 .095

Probability of selecting the best arm declines as the number of arms increases

P{X1>max(X2,…,Xk)}

= x P(X1=x)P(X2<x, X3<x…,Xk<x|X1=x)

= x P(X1=x)P(X2<x) P(X3<x)… P(Xk<x)

= x P(X1=x)P(X2<x)k-1

if X2, …, Xk have the same distribution


X1~Bin(50,.32); X2,…,Xk~Bin(50,.20) gives P{X1>max(X2,…,Xk)} = .90 for k = 2 andP{X1>max(X2,…,Xk)} = .72 for k = 6

Advanced renal trial of several targeted agents: 6 arms, n=55 / arm◦ TTP compared via Cox model◦ If one arm has median TTP of 7.2 months and the

other 5 have median TTP of 4.8 months (50% improvement), then the probability of selecting the best arm is 0.87

Randomized Selection(Pick-the-Winner) Design

Discussed for evaluating cytostatic agents in Korn et al. (2001)

Randomize experimental vs. standard and formally compare the arms

Appropriate if don’t have a reasonable prior estimate of expected control arm outcomes

Endpoint could be any of the standard phase II endpoints (e.g. TTP, response)

Might target larger differences than a phase III

Comparative Randomized Control Design

Test could be a definitive (phase III) evaluation with < 0.025 (one-sided)◦ If little prior phase II efficacy data, need early

stopping rules for lack of benefit◦ Might not be appropriate if a second phase III

study evaluating survival would be needed


Test could be a suggestive (phase II) evaluation with a larger (e.g. 0.10 to 0.20)◦ Appropriate for screening new agents◦ If positive, still needs to be followed by a

definitive phase III study◦ Korn et al. suggest using = 0.20, because the

sample size with = 0.10 is large enough that it might be better to go directly to the definitive study


3-arm comparison of TTP (two dose levels of bevacizumab), targeting a large difference (100% improvement in median TTP), but designed to be definitive (Yang, 2003)◦ Overall = .05 (two-sided), = 0.20◦ Each comparison at one-sided 0.0125◦ Needed about 50 patients / arm (stopped early

because of highly significant results)◦ Crossover from placebo to low dose drug

Study of Bevacizumab vs. Placebo in RCC

Was overall = .05 appropriate?◦ A second, larger study is still needed for

survival◦ Could have identified drug as promising with

even fewer patients (larger )

Yang Study of Bevacizumab vs. Placebo

Was a placebo needed?◦ Evaluation bias should be much smaller than a

doubling of TTP◦ May not be to identify promising drugs◦ FDA tends to require a placebo for TTP

Was the control arm needed?◦ Would results from a single arm, single

institution study have been convincing?

Yang Study of Bevacizumab vs. Placebo

Cisplatin + C225 vs. Cisplatin + Placebo Designed to have 90% power to detect an

improvement in median PFS from 2 months to 4 months (100% improvement) with = 0.025 (one-sided)

With allowance for non-compliance, required 54 eligible patients / arm

Final accrual was 117 eligible patients

E5397 – Advanced Head and Neck Cancer

E5397 Summary of Results

Cisplatin + C225

Cisplatin + Placebo

P-value (one-sided)

Response Rate 26% 10% 0.02

Median PFS 4.2 mos 2.7 mos 0.09

Median Survival 9.2 mos 8.0 mos 0.21

Hazard Ratios (Placebo/C225) and 95% CIs

PFS: 1.31 (0.91, 1.89)

Survival: 1.16 (0.80, 1.69)

E5397 PFS by Treatment

0 5 10 15 20

0.0

0.2

0.4

0.6

0.8

1.0

Months

Pro

bab

ility

Cisplatin+C225 (55 events/ 56 cases)Cisplatin+Plcbo (59 events/ 60 cases)P1 = 0.09

E5397 Survival by Treatment

0 5 10 15 20 25 30

0.0

0.2

0.4

0.6

0.8

1.0

Months

Pro

bab

ility

Cisplatin+C225 (54 events/ 57 cases)Cisplatin+Plcbo (57 events/ 60 cases)P1 = 0.21

Study is not definitive – underpowered for both PFS and survival

Is it promising – should a follow-up study of C225 be done?

Would a better strategy have been a single arm phase II with a response endpoint, followed by a definitive phase III based on the ‘promising’ response rate of 26%?

E5397

PFS reaches the one-sided = 0.10 cutoff for a ‘promising’ phase II result

Survival would not have been an appropriate endpoint◦ Estimated improvement is 16%◦ Confidence interval consistent with 20% decrease

to a 69% increase◦ Phase II sample sizes are not adequate to detect

realistic survival effects

E5397

An enrichment strategy based on randomizing patients who appear to be doing well on the treatment (Rosner, Stadler, Ratain, 2002)

Initially all patients are treated, patients free of progression for some period of time are randomized between continuing treatment and placebo, with crossover from placebo to treatment at progression or specified PFI

Complex design with a blinded randomization and 3 registration points

Randomized Discontinuation Design


RANDOMIZE

RX

RXPlacebo

REGISTER

REASSES

Initial RX

Off study

PD

SD

Continue RXResponse

Crossover at PD or after

specified PFI

(run-in)

Usefulness depends on how successful the run-in is in selecting patients benefiting from treatment◦ TTP is highly variable in most diseases, so

randomized population will be a mixture

◦ Korn et al. (2001), Capra (2004) suggest often less efficient than standard RCT

Carry-over effect could dilute difference between randomized arms

Requires much larger sample size


CALGB 69901 (CAI in RCC) Randomize patients if stable after 16 weeks Enrolled 374 patients; randomized 65

eligible patients (17%)◦ Enrichment strategy was not successful, but does

CAI have any activity?◦ Did they learn any more from 374 patients than

ECOG did from 57 patients in a more traditional two-stage phase II design (E4896)?


In many settings, conventional phase II designs may still be appropriate

Start-up costs for single-arm two-stage designs are a concern

Randomized phase II studies allow evaluation of multiple agents or schedules and protect against sampling bias

Selection designs are useful for informal comparison and identifying promising agents

Main Points

Control arms should not ordinarily be needed, but can be effective in some settings

Survival is seldom (never?) the best phase II endpoint

Randomized discontinuation designs may not be appropriate and need to be strongly justified

Main Points

Capra WB (2004). Comparing the power of the discontinuation design to that of the classic randomized design on time-to-event endpoints. Controlled Clinical Trials 25:168-177.

Freidlin B, Dancey J, Korn EL, Zee B, Eisenhauer E (2002) Multinomial phase II trial designs (letter to the editor). Journal of Clinical Oncology 20:599.

Korn EL, Arbuck SG, Pluda JM, Simon R, Kaplan RS, Christian MC (2001). Clinical trial designs for cytostatic agents: are new approaches needed? Journal of Clinical Oncology 19:265-272.

Rosner GL, Stadler W, Ratain MJ (2002). Randomized discontinuation design: application to cytostatic antineoplastic agents. Journal of Clinical Oncology 20:4478-4484.

Simon R (1989). Optimal two-stage designs for phase II clinical trials. Controlled Clinical Trials 10:1-10.

Simon R, Wittes RE, Ellenberg SS (1985). Randomized phase II clinical trials. Cancer Treatment Reports 12:1375-1381.

Yang JC et al. (2003). A randomized trial of bevacizumab, an anti-vascular endothelial growth factor antibody, for metastatic renal cancer. New England Journal of Medicine 349:427-434.

References

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