phase ii STUDY designs: - PowerPoint Presentation

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phase ii STUDY designs: Part 2Resident and Fellows Lecture SeriesApril 12, 2016Elizabeth Garrett-Mayer, PhDHollings Cancer Center

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Current State of Phase IILots of changes in the past several yearsPhase I  Phase II  Phase III  FDA approval ??Ceritinib: approved based on “phase I” trial data.Nivolumab phase I trial: 94 melanoma patients (5 doses)76 NSCLC patients (3 doses)

Pembrolizumab

phase I trial: >1200 patients

Phase I objectives have changed…what happens to phase II objectives?Slide3

Goals of Phase II StudiesProvide initial assessment of efficacy or ‘clinical activity’Screen out ineffective drugsIdentify promising new drugs for further evaluationFurther define safety and toxicity TypeFrequencyNewer goals:Define patient subgroups that respond to therapyIdentify need for biomarker-driven phase III designIdentify correct dose for further study based on efficacySlide4

Single Arm Phase I StudiesComparison is “fixed” constantBinary endpoint (clinical response vs. no response)Often one-sided testSimple set-up:Slide5

Phase II studies: Testing a hypothesis Null hypothesis: H0Usually the status quo (e.g. current response rate under the standard of care)If this is the response rate, then the treatment is not worth pursuingAlternative hypothesis: H1 or HaThe expected response rate with this new treatment.Or, lowest response rate we would consider sufficient for further study of the treatment.At the end of the study, we hope to reject the null hypothesis.Slide6

Review of alpha and power









Type I error

Alpha =

α

= probability

of Type I error (level of significance)

Beta =

β

= probability

of Type II error

Power =

1 -

β

Accept H

o

Reject H

o

H

o

is True

H

o

is NOT True

Type II errorSlide7

Single Arm Phase I StudiesComparison is “fixed” constantBinary endpoint (clinical response vs. no response)Often one-sided testSimple set-up:

Based on design parameters:

N=39

Conclude effective if 12 or more responses (i.e., observed response rate of ≥0.31)Slide8

Single Arm Phase I StudiesWhat if by the 15th patient you’ve seen no responses?Is it worth proceeding?Maybe you should have considered a design with an early stopping ruleTwo-stage designs:

Stage 1:

enroll N

1

patients

X

1

or more respond

Stage 2: Enroll an

additional N

2

patients

Stop trial

Fewer than X

1

respondSlide9

Revised Design: Simon Two-StageStage 1: enroll 19 patientsIf 4 or more respond, proceed to stage 2If 3 or fewer respond, stopStage 2: enroll 20 more patients (total N=39)If 12 or more of total respond, conclude effectiveIf 11 or fewer of total respond, conclude ineffectiveDesign properties? What about power? Slide10

Question 1:The power of this two stage design is:higher than in the single stage studylower than in the single stage studythe same as in the single stage studyI can’t remember what power is…Slide11

Two-stage designsSimon two-stage (1989)Used in exampleMANY designs fit the criteria“Optimal”Minimum expected sample size under H0Minimum maximum sample sizePreserves alpha and power, and permits early lookOther two stage designsGehan two-stage: first stage always stops if 0 responses

Balanced design: sample size in first and second stages are the same (or close). Ye and Shyr, 2007

Fleming two-stage: allows for early stopping for efficacy (not just futility).Slide12

Multi-stage designsPredictive probability design (Liu and Lee, 2008)Bayesian DesignLooks multiple times during the study to stop for futilityStopping is based on the probability of a successful trial at the end of the trial based on the accumulated data so far in the trial.Sequential designs (frequentist)Allow multiple looks at the dataControl the timing of looks and ‘spends’ some of the type I or II error at each look.O’Brien-Fleming spending functions.Slide13

Early StoppingFUTILITY stoppingMost of the designs discussed so far ONLY allow stopping if there is strong evidence that the treatment is not efficaciousCan also have early stopping for efficacyGenerally not popular in single arm studiesImportant to accumulate evidence to support claim of efficacyBut, not stopping prolongs time to launch phase IIISlide14

Safety stoppingDon’t forget to consider early stopping plans for excessive toxicityOften there is little information about safety of the phase II dose.What if the dose is too toxic?Data safety monitoring planCan be rule based and included in the protocolOften times there is an independent data safety committee that reviews safety dataSlide15

Frequentist vs. BayesianFrequentists: α and β errorsBayesians:

Quantify designs with other properties

General philosophy

Start with prior information (“prior distribution”)

Observe data (“likelihood function”)

Combine prior and data to get “posterior” distribution

Make inferences based on posteriorSlide16

Bayesian inferenceNo p-values and confidence intervalsFrom the posterior distribution: Posterior probabilitiesPredictive probabilities (e.g., Liu and Lee, 2008)Prediction intervalsCredible intervalsBayesian designsCan look at data as often as you like (!)Use information as it accumulates

Make “what if?” calculations

Helps decide to stop now or notSlide17

Bayesian DesignsRequires ‘prior’Reflects uncertainty about the response rateCan be ‘vague’, ‘uninformative’Can be controversial: inference may change

Prior Distribution

Response RateSlide18

Question 2:Which prior makes the most sense?

1

2

3

4Slide19

Bayesian design exampleSlide20

Posterior ProbabilitiesSlide21

Other priorsWhat if we had used a different prior?Assume informative “orange” priorSlide22

What about PFS as an endpoint?Progression-free survival as the primary endpoint in a single arm phase II trial has been controversialWhy?Slow growing cancer cannot be distinguished from truly stable or modestly shrinking tumor burden. Recall RECIST definition of stable disease. Interval censoring (next two slides).With biomarker defined patient populations, there may not be a good comparator study as a historical control. Without a comparator arm, the inferences due to the above issues are tricky. Slide23

Randomized Phase II Trial of Ridaforolimus in AdvancedEndometrial Carcinoma (Oza et al, JCO June 2015)

http://jco.ascopubs.org/content/early/2015/06/15/JCO.2014.58.8871Slide24

Interval-censoringProgression is generally observed at 2 to 3 month intervals at clinic visits.“8 week” clinic visit may occur at 7.5 weeks, 8.1 weeks, 9 weeks, etc.Slide25

Alternative to PFS in single arm study?If lack of progression is of interest, consider a “clinical benefit” (i.e. CR or PR or SD) at a fixed point in time.The “time” should be defined by a clinic visit. Example:The primary endpoint is clinical benefit, defined as having complete or partial response, or stable disease at the 4 month visit after initiation of therapyThe 4 month visit may occur with +/- one week of 4 months on study.Slide26

Randomized Phase II StudiesWhy randomized?Want some “control” or “prioritization”Primarily two different kinds of randomized phase II studiesPhase II selection design (prioritization)With recent immunotherapies, this approach is becoming a more attractive optionMultiple doses from phase I need to be exploredThese are often done in conjunction with a Phase IPhase II designs with reference control arm (control)Slide27

Phase II selection design (prioritization)Two parallel one arm studies (classic case)Do not directly compare arms to each other.Compare each to “null rate”Why? To compare to each other, you’ll need a study at least two times as large. Nivolumab, Pembrolizumab,

etc

:

The expansion cohorts ended up as randomized selection designs

Doses of the same agent were evaluated for optimal efficacy but not statistically compared.

Appropriate to use when:

Selecting among multiple new agents

Selecting among different schedules or dosesSlide28

“Pick the Winner” designs for selection (Simon et al., 1985; Sargent & Goldberg, 2001 )NOT appropriate when trying to directly compare treatment efficacies (not powered)Simon et al: Uses 2+ Simon two-stage designsAm arm must satisfy efficacy criteria of Simon design

Move the “winner” to phase III

Only have to pick winner if more than one arm shows

efficacy

Sargent & Goldberg:

Winner has to win by a margin, d, for the primary outcome.

Otherwise, additional information (e.g. safety, other clinical outcomes) is used to choose the winner.

Can be used when the goal is prioritizing which (if any) experimental regimen

or dose should

move to

the next phase when

no

a priori

information to favor one. Slide29

Randomized Phase II designs with reference arm (control)Includes reference arm to ensure that historical rate is “on target”Reference arm is not statistically compared to experimental arm(s) due to small NFrequent goal is to get information for planning a phase III trial.

Be careful not to confound the phase III endpoint.

Example: Randomized phase II with a cross-over option.

At progression, patients can switch from standard of care arm to novel treatment.

What happens to assessment of overall survival?Slide30

Randomized Phase II: Stopping RulesSimilar considerations for ‘early stopping’ should be included in randomized phase II studiesStopping is often specific to terminating one or more arms.If one arm is standard of care, stopping rules should:Consider if the novel treatment arm outcomes are significantly worse than standard of care armEnsure that patients in novel treatment arm are not experiencing unacceptably high toxicity due to treatment.Slide31

Question 3: Adaptive TrialsAdaptive trials:Always use Bayesian designsAllow the study team to alter the planned sample size based on low accrual even if they did not plan to change the sample size in advanceAllow prospectively planned modifications of aspects of the study design.Have smaller sample sizes than non-adaptive trials.Ensure patients will have a better chance of getting a more effective therapy.

3, 4, and 5

All of the aboveSlide32

FDA’s draft guidance on adaptive designs in drug development (Feb 2010) …, an adaptive design clinical study is defined as a study that includes aprospectively planned and specified modification or potential for modificationof one or more specified aspects of the study design and hypotheses, basedon analysis of data from subjects in the study. Analyses of the accumulatingstudy data are often performed at prospectively planned points within thestudy, may be performed in a fully blinded manner or in a non-blindedmanner, …

The term “prospective” here means that the adaptation was planned before

data were examined in an

unblinded

form by any personnel involved in

planning for the revision.

Revisions

made or proposed after an

unblindedinterim analysis raise major concerns about study integrity, possibledesign changes need to be

prospectively defined

and carefully implemented

to avoid risking irresolvable uncertainty in the interpretation of study results.

Robert T. O’Neill, PhD / OTS, CDER, FDASlide33

FDA’s draft guidance on adaptive designs in drug development (Feb 2010)The range of possible study design modifications that can be planned in the prospectively written protocol is broad. Examples include changes in the following: • study eligibility criteria (either for subsequent study enrollment or for a subset selection of an analytic population) • randomization procedure

•

treatment regimens of the different study groups (e.g., dose level,

schedule)

•

total sample size of the study (including early termination)

•

concomitant treatments used

•

planned schedule of patient evaluations for data collection

•

primary endpoint (e.g., which of several types of outcome assessments, which

timepoint

of assessment)

•

selection and/or order of secondary endpoints

• analytic methods to evaluate the endpoints (e.g., covariates of final analysis, statistical

methodology, Type I error control) Slide34

Adaptive RandomizationRandomization is “adapted” based on accumulated informationAdaptive on Outcome Assign treatments according to accumulated information about best treatment. Assign with higher probabilities to better therapiesSlide35

Example: Randomized phase II trial with two treatments: A and BSample size is 100First 30 patients are evenly randomized (15 per arm) After 30 patients, patients are adaptively randomized:Arm with the higher response rate has a higher likelihood of assignment of next patientHow much higher depends on: The difference in response ratesA ‘tuning parameter’ that is determined as part of the trial planning to ensure that imbalance is not too large. Can depend on the current sample size.Slide36
Slide37

Standard Design

Adaptive Randomization

Begin

by randomizing

with

equal chance per

arm.

Then

, adjust probability

of

assignment to reflect

the

knowledge of the

best

treatment.

Stopping rules: drop an arm when

there

is “strong” evidence that

It has low efficacy OR

It has lower efficacy than

competing treatments

RANDOMIZE

Idarubicin

Ara-C

TroxIdarubicin

Trox

Ara-C

N=25

N=25

N=25

N=?

N=?

N=?Troxacitabine

in AML (Giles et al. 2003)Slide38

Adaptive Design: Troxacitabine in AMLSummary of trial results:TI dropped after 24th patientTrial stopped after 34 patients (TA dropped)

IA

10/18 = 56%

TA

3/11 = 27%

TI

0/5 = 0%

Complete responses by 50 daysSlide39

Buyer bewareAdaptive randomization does not always perform well and can lead to biased estimatesDesigns need to be fully vettedSee Thall et al. 2015

Pathologic example: N=15 in Arm A

N=85 for Arm BSlide40

Baskets and UmbrellasRecent years have seen an emphasis on molecular groupings of tumorsUmbrella trials: Basket trials:

Cancer type (e.g. NSCLC)

Molecular signature A

Molecular signature D

Molecular signature B

Molecular signature C

Lung

Colon

BreastSlide41

Umbrella Trial: Personalizing Therapy for Non-Small Cell Lung Cancer

BATTLE:

B

iomarker-integrated

A

pproaches of

T

argeted

T

herapy of

L

ung

Cancer

E

limination

Kim ES et al, Cancer Discovery 2011; 1(1): 44-53Slide42

BATTLE TrialPatients of same cancer type (here NSCLC) assigned different treatments based on molecular profilePrimary endpoint = Disease control at eight weeks Four parallel phase II studiesEqual randomization followed by adaptive randomization Based on disease control rate for molecular subgroupPatients are assigned at higher probability to treatment with higher disease control rate for their molecular profile.

Slide courtesy of Betsy HillSlide43

Slide from Jack Lee:

http

://www.winsymposium.org/wp-content/uploads/2013/09/L-3.02-J.-Jack-Lee.pdfSlide44

Kim ES et al, Cancer Discovery 2011; 1(1): 44-53Slide45

Other Umbrella TrialsBATTLE-2I-SPYI-SPY-2http://www.ispy2trial.org/Slide46

What is a basket trial?New and evolving form of trial design based on hypothesis that presence of a molecular marker predicts response to targeted therapy independent of tumor histology.Mutations identified, patients assigned to specific treatment arm (or randomization to subset of treatments) based on mutation statusSeveral independent parallel phase II trials in one study~10-15 patients per disease typeTreatment assignment based on molecular profile

Redig

and

Janne

, JCO, Feb 9

2015.Slide47

Basket TrialsBasket design may start with the use of a targeted therapy in an unselected population followed by Next Gen Seq (NGS) in patients who respond to identify genetic biomarkers for subsequent prospective screening.Basket trials have generated an enormous amount of interest: they implement a hypothesis-driven strategy incorporating precision medicine into clinical trials even for mutations that are rare or difficult to study solely within a disease-specific context.

Redig

and

Janne

, JCO, Feb 9

2015.Slide48

Example trial: CUSTOMMolecular Profiling and Targeted Therapies in Advanced Thoracic Malignancies Trial Seeking to identify molecular biomarkers in (1) advanced NSCLC, (2) small cell lung cancer, and (3) thymic malignancies5 targeted agents:Erlotinib against EGFR mutationsSelumetinib (MEK inhibitor) against KRAS, HRAS, NRAS and BRAF mutationsMK2206 (AKT inhibitor) against PIK3CA, AKT1 and PTEN mutationsLapatinib against HER2 mutationsSunitinib

against KIT and PDGFRA

mtuations

.

FIFTEEN (3 x 5) study arms.

Lopez-Chavez et al., JCO, Feb 9, 2015 (

epub

ahead of print)Slide49

CUSTOM statsEach arm is an independent phase II trial using an optimal Simon two-stage design.14 arms: null response rate = 10%; alternative = 40%EGFR mutant NSCLC arm: null response rate = 30%; alternative = 60%.Lopez-Chavez et al., JCO, Feb 9, 2015 (epub ahead of print)Slide50

CUSTOMLopez-Chavez et al., JCO, Feb 9, 2015 (epub ahead of print)Slide51

Lopez-Chavez et al., JCO, Feb 9, 2015 (epub ahead of print)Slide52

StrengthsAbility to identify favorable response to targeted therapy with small N and validate targetOnly 15 pts with NSCLC with EGFR mutation enrolled, yet significant results (60% ORR).(However, Arm closed early: overwhelming evidence of efficacy of erlotinib elsewhere)Proof-of-principle validation of putative targetSlide53

ChallengesAccrualPrevalence of Mutations

Lopez-Chavez et al., JCO, Feb 9, 2015

Figure 2: Frequency of

genetic abnormalities in

(A) NSCLC and (B) small

cell lung cancer.Slide54

Other basket trialsNCI-MATCH:Plans to screen 3000 pts with enrollment of at least 1000 pts for targeted drug combinations. Independent of histology. Launching early 2015.Expected to have 20 different treatments, 20 pharma companies, and as many as 2400 sites.NCI-IMPACT:Randomly assigns pts with a mutation in specific genetic pathway to either targeted therapy for pathway or treatment not known to be pathway specific (NCT0182784)Slide55

Basket Trial SuccessSuccess depends on strength of data linking target and targeted therapyTwo key conditions:Tumor must depend on pathwayTargeted therapy must be able to reliably inhibit the targetSlide56

SummaryPhase II is changing.Lots of talk about meshing Phase I/II/III so keep your eyes and ears open.Consider the following when designing your study:Early stopping for futility and toxicityAccrual….harder than you think!Adaptive design? Performance characteristics are important.Talk to your statistician as early as possible in the planning stages.Slide57

References Ceritinib phase I trial: Shaw et al., NEJM 2014; 370:1189-1197.Nivolumab trials: Topalian et al., NEJM 2012; 366 (26):2443-2454 (see also trial protocol as supplementary material)Topalian et al., JCO 2014; 32(10): 1020-1031Phase II design guidance: Seymour et al., Clinical Cancer Research 2010; 16(6):1764-1769.Two-stage designs Simon R. Control Clin Trials. 1989 Mar;10(1):1-10.Gehan

EA. J Chronic Dis. 1961 Apr;13:346-53

Ye

and Shyr. Clinical Trials 2007; 4(5): 514-24.

FDA draft guidance on adaptive designs:

http://www.fda.gov/downloads/Drugs/Guidances/ucm201790.pdf

Predictive probability design: Liu and Lee, Clinical Trials. 2008; 5(2):93-106.

PFS example: Oza

et al., JCO 2015;

Epub

ahead of print.Slide58

References Randomized prioritization designs: Simon R, Wittes RE, Ellenberg SE. Cancer Treatment Rep 1985;69:1375–1381. Sargent D and Golberg R. Statistics in Medicine 2001; 20: 1051-1060.Adaptive Randomization example: Giles FJ et al. J Clin Oncol. 2003 May 1;21(9):1722-7.Adaptive randomization:

Thall

PF and

Wathen

JK European Journal of Cancer 2006;

Dangers of AR:

Thall

PF et al., Annals of Oncology 2015, Epub ahead of print. doi:10.1093/annonc/mdv238BATTLE trial:

Kim

ES et al, Cancer Discovery 2011; 1(1): 44-53.

Zhao, et al. Clinical Trials 2008; 5:181-193

Basket Trials :

Redig

and

Janne

, JCO, Feb 9 2015.Lopez-Chavez et al., JCO, Feb 9, 2015 (epub ahead of print)