advanced statistical considerations for early phase trials · 2018. 12. 14. · test for...

Advanced Statistical Considerations for Early Phase Trials

J. Jack Lee, Ph.D. (李君愷)Professor, Department of Biostatistics

OutlinePhase I/II Designs– Eff-Tox design, Multc-lean design– BOP2 design

Bayesian Interim Monitoring – Toxicity Monitoring via posterior probability– Efficacy monitoring via posterior probability– Efficacy monitoring via predictive probability

Design for Identifying OBDRevolution of Immuno-oncology Trials Past, Present, and Future of Cancer TrialsSteps for Success– Keep it smart and simple– Need user-friendly software for the design & implementation

EFF-TOX: Phase I/II DesignDose finding based on efficacy/toxicity trade-offs (EFF-TOX)– Define a dose level x as acceptable if

– For example, Prob(Response rate > 0.3) > 0.1 & Prob(toxicity rate < 0.4) > 0.1

– Derive an efficacy toxicity trade-off contour on a two-dimensional outcome space

Thall and Cook, Biometrics 2004;60:684-693

Pr{ ( , ) | ) andPr{ ( , ) | )

E E E

T T T

x D px D p

3

4

EFF-TOX: Dose Finding with Efficacy & Toxicity Trade-offs

Enroll patients in cohorts1. Treat the first cohort at the starting dose2. Observe the outcome, then, update the prob of efficacy and

toxicity3. Determine the acceptable doses among the pre-specified

doses4. Enroll the next cohort into the most desirable dose

according to the EFF-TOX tradeoff contour5. Stop the trial when there is no acceptable doses or reaches

max(N) 6. Otherwise, go to 2.

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Multc Lean: Toxicity and EfficacyPhase II study simultaneously controls both toxicity and efficacyA toxicity rate of 0.2 is acceptable but a rate of 0.4 is unacceptable. Assume that the information from the standard arm with a response rate of 0.3 and a toxicity rate of 0.2Early stopping for toxicity but not efficacyAssume the maximum sample size of the trial is 75.Provide the operating characteristics for a new treatment with:1. response rate =0.3 and a toxicity rate= 0.42. response rate =0.4 and a toxicity rate= 0.2

Thall, Simon, and Estey, Bayesian sequential monitoring designs for single arm clinical trials with multiple outcomes, Statistics in Medicine, 14:357-379, 1995).

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BOP2: A Bayesian Optimal Design for Phase 2Clinical Trials with simple & complex endpointsProvides a unified framework for phase II trials with simple and complex efficacy and toxicity endpoints.Explicitly controls the type I (and II) error rates.Is optimal by (i) maximizing power, given a fixed N and type I error; or (ii) minimizing the E(N|H0), given fixed type I and II error rates.Easy to use software is freely available to generate stopping boundaries, operating characteristics and protocol for the BOP2 design.

Zhou H, Lee JJ, Yuan Y. BOP2: Bayesian optimal design for phase II clinical trials with simple and complex endpoints. Stat Med. e-Pub 6/2017. PMID: 28589563.

BOP2 Design, ExamplesExample 1: A treatment is – futile if ORR ≤ 0.2; promising if ORR ≥ 0.4.

Example 2: A treatment – Fails if CR ≤ 0.15 or CR+PR ≤ 0.30.– Succeeds if CR ≥ 0.25 or CR+PR ≥ 0.50.

Example 3: A treatment– Fails if ORR ≤ 10% and PFS6 ≤ 20%.– Succeeds if ORR ≥ 30% or PFS6 ≥ 35%.

Example 4: A treatment is safe and efficacious if– Null: ORR ≥ 45% or toxicity rate ≤ 30%.– Alternative: ORR ≥ 60% and toxicity rate ≤ 20%.

Stopping Boundaries for BOP2 Design

Bayesian Toxicity Monitoring

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Video: Bayesian Toxicity Monitoring

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Stopping Boundaries

Operating Characteristics

Operating Characteristics

Trial Monitoring

Design for Identifying OBDFor molecularly targeted agents & immune agents, little toxicity may arise within the therapeutic dose range. The dose–response curves may not be monotonic.The goal is to find the optimal biological dose (OBD), which is defined as the lowest dose with the highest rate of efficacy while safe.Apply nonparametric and uses the isotonic regression to identify the optimal biological dose.Identify “admissible” with acceptable toxicities. Assign patients to the dose with highest efficacy probabilities.Repeat the process until the Max(N) is reached.

Revolution of Immuno-Oncology TrialsChoice of endpointsThe use of dose-expansion cohortIdentify predictive markersHow to combine with chemotherapy, radiotherapy and/or targeted agents?

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Choice of EndpointsResponse rate– Delay response

Response durationProgression-free survival– Initial false progression

Overall survivalImmune markers– Immune cell infiltration– TCR– Change of markers before and after treatment

Pembrolizumab Trial: KEYNOTE-001Pembrolizumab is a PD-1 inhibitor.First-in-human trial, initiated in 2011, to determine the RP2D in patients with advanced solid tumorsStriking responses in initially enrolled metastatic melanoma pts. Prompted an increase in sample size for an evaluation of overall response rate and disease control rate in melanoma patients.Additionally, potential activity in NSCLC prompted the addition of a cohort to assess overall response rate in lung cancer. As promising results were obtained with each additional cohort, the trial continued to be expanded.Ultimately over 1,200 patients were treated in this trial. – One cohort had 173 patients with unresectable or metastatic melanoma who were

randomized to two different doses of pembrolizumab. Efficacy results were sufficient for accelerated approval in September 2014 - just three years after the initiation of the first-in-human trial

– Subsequently, data from the lung cohorts led to approval in lung cancer as well as the approval of a companion diagnostic for tumor PD-L1 expression

Theoret et al. Expansion Cohorts in First-in-Human Solid Tumor Oncology Trials. CCR 2015

Mullard, Nature Reviews Drug Discovery 2016

Largest Phase I Oncology Trials

Mullard, Nature Reviews Drug Discovery 2016

Remarks for the Dose Expansion CohortNeed compelling rationale for having multiple expansion cohortsNeed to define the specific objective and choose the proper endpoints (toxicity and efficacy)Need to provide sample size justification– Need a statistical design when N ≥ 30. (reaching to the

sample size for a single-arm Phase II study)Is there an early stopping rule? – Toxicity, Futility, Efficacy

Discuss with regulatory agency early and oftenNeed to have an independent data safety and efficacy monitoring board

Identify Predictive Markers –Medical perspective

Treatment– Different PD-1 inhibitors– Different dose, schedule

Patient population– Demographics: age, sex, gender, smoking history– Disease state: histology, performance status, first-line or

prior treated (prior treatments)– Immune profile: PD-L1, IFN- gene signature– Molecular profile: tumor mutation burden (TMB),

microsatellite instability (MSI) Biomarker assay– IHC staining: different antibodies– NGS platforms

Identify Predictive Markers – Statistical perspective

Endpoint selection– Response rate, PFS, OS

Analysis method– Continuous marker, transformation, dichotomize– How to choose the cut-off point?– Multi-covariate analysis, model interactions

Synthesizing information– Meta-analysis, meta-regression, network meta-analysis

Multiple testing– Subset analysis, adjust for multiplicity– Validation

Internal, external, prospective

Finding the Right Biomarkers First-line immunotherapy of NSCLC− Merck, KEYNOTE-024 Trial: pembrolizumab vs. platinum-based chemo− PD-L1 (+): ≥ 50%, N=305

Reck et al., NEJM 2016

KEYNOTE-024 Trial

Reck et al., NEJM 2016

Checkmate 026 Trial

Checkmate 026: PFS by Tumor Mutation Burden

(>243 mutations) (<100; 100-242 mutations)

Treatment x TMB Interaction?

Checkmate 026: ORR by TMB and PD-L1

Objective Response Rate by PD-L1 Cut-point

Khunger et al., JCO Precision Oncology 2017

1% Cut-off

2.17

5% Cut-off

2.80

10% Cut-off

2.84

RE Model

0.2 1 5 10

Odds Ratio (log scale)

Atezolizumab, Besse

Atezolizumab, Spira

Atezolizumab, Spigel

Pembrolizumab, Garon

Pembrolizumab, Herbst

Pembrolizumab, Garon

Durvalumab, Rizvi

Avelumab, Gulley

Nivolumab, Borghaei

Nivolumab, Brahmer

Nivolumab, Rizvi

Nivolumab, Gettinger

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50

50

50

50

50

25

5

5

5

5

5

79

9

14

31

87

49

23

18

32

9

6

5

223

15

39

42

203

70

61

104

63

33

19

28

36

8

12

17

40

31

5

2

19

11

7

5

321

61

72

86

360

206

87

18

117

64

44

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3.16 [2.06, 4.85]

4.57 [1.51, 13.84]

2.15 [0.91, 5.11]

3.73 [1.86, 7.50]

3.86 [2.55, 5.82]

4.65 [2.75, 7.86]

6.56 [2.36, 18.21]

1.56 [0.33, 7.30]

3.13 [1.64, 5.96]

1.59 [0.60, 4.21]

1.98 [0.59, 6.70]

1.07 [0.28, 4.10]

3.31 [2.66, 4.11]

Response No Response No

High PD-L1 LOW PD-L1Cutoff PctDrug and author

Odd Ratio [95% CI]

tau^2 estimate=0.0141 (se=0.0628), method:EBTest for Heterogeneity: Q(df=11)=11.8106, p_value=0.3780

Log OR estimate=1.196, se=0.1108, p_value<0.0001

Meta-analysis From 12 Trials

Data Taken from Grigg and Rizvie, J for Immu Therapy of Cancer 2016

1% 5% 10% 25% 50% 75%

PD-L1 Cut-Off

Obj

ectiv

e R

espo

nse

Rat

e

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.240.27

0.32

0.420.46

0.54

N= 637 N= 242 N= 122 N= 138 N= 160 N= 72

Objective Response Rate by PD-L1 Cut-point

Adapted from Aguiar Jr et al., Immunology 2016

Aggregated Data From 11 Trials

Objective Response Rate by PD-L1 Cut-point

Grigg and Rizvie, J for Immu Therapy of Cancer 2016

Grigg and Rizvie, J for Immu Therapy of Cancer 2016

Tumor Mutation Burden as A Marker

(Durable Clinical Benefit) (No Durable Benefit)

Rizvi, Science 2015

Rizvi, Science

IFN- Gene Expression as A Marker(in Head and Neck Squamous Cell Carcinoma)

Ayers, et al., JCI 2017

Ayers, et al., JCI 2017

Biomarkers SummaryBiological, mechanism-based biomarkers are useful for immuno-oncology drug development.– Preclinical testing for biomarker data

Cell lines, syngeneic mouse model, PDX, etc.

No shortage of methods for biomarker discovery– Select functional form of the biomarker

Continuous, dichotomizing, cut-point selectionCheck Martingale residual, with or without adding other covariates

– Variable selection, Multiple regressionMulti-covariable analysis

– Control type I error and/or false discovery rateValidation is the key– Many are called; few are chosen

Internal, external, independent validation

Past, Present, and Future of Cancer Trials

Past: One trial, one drug, one patient population at a time expensive, high failure rate – Disjoint process, non-targeted agents, no patient selection

based on markers– Fixed design, infrequent interim analyses– Rush into Phase III too early

Present: biomarker integrated– Biomarker-based patient selection and drug matching– All comers, “master protocol” trials, platform trials

Future: smart trials– More adaptive designs in Phase I and Phase II trials– Smaller, more focused Phase III trials with higher success

rates.

3 Key Elements for Contemporary TrialsDiscovery– Keep your eyes open. Look for interesting patterns.– Be aware of multiplicity issues and false positive findings.– Exploratory: Hypothesis generating

Validation– Internal validation, external validation, prospective validation– Confirmatory: Hypothesis testing

Being Adaptive– We learn as we go.– Continuous learning: cycle between discovery and validation– Platform trials, take all comers– Perpetual trials, continuous learning and improving

Implement and make a difference!

Steps for SuccessKeep it simple and smart (KISS)Be adaptive– We learn as we go.– Frequent decision making on treatment assignment,

early stopping on toxicity, futility, and/or efficacyNeed user-friendly software for the study design and conduct

http://www.trialdesign.org/

Clinical Trial Design Software

http://www.trialdesign.org/

https://biostatistics.mdanderson.org/softwareDownload/

80+Programs Freely Available!

https://biostatistics.mdanderson.org/softwareOnline/

Secret of Life

Avoid brute force! Work with a statistician!

Be adaptive! Be smart!

Let’s do it !

Implement and make a difference!

Team Work!

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