Transcript Phase II designs in Oncology

Nick Pavlakis, MBBS, MMed (Clin Epi), PhD
Royal North Shore Hospital
Sydney University
PHASE II DESIGNS IN ONCOLOGY
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Outline
• Aims of a Phase II study
• Design aspects
– Disease/population selection
– Endpoint selection
– Single-arm vs randomised design
– Enrichment strategies involving biomarkers
– Statistical design aspects: Traditional, 2-stage vs adaptive/Bayesian
• Case example
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Outline
• Aims of a Phase II study
• Design aspects
– Disease/population selection
– Endpoint selection
– Single-arm vs randomised design
– Enrichment strategies involving biomarkers
– Statistical design aspects: Traditional, 2-stage vs adaptive/Bayesian
• Case example
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Clinicians’ perspective for Phase II trials
• Why do a Phase II Trial?
– To determine the activity of a drug (efficacy) in a given tumour type or types
(“screening”)
• Seek preliminary hints of activity and guide selection of tumour types for further study
– To determine safety of the drug
• In a specific patient population/disease setting
• Part of strategic pharma development, ie, Phase II following Phase I (“decision
making”)
– Go/no-go answer to allow the conduct of a definitive
Phase III/registration trial in a specific disease
• Co-operative group or investigator-initiated
– Clinically driven rationale/unmet need
– Single agent or combination with existing therapy
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Aims of a Phase II study
• Provide initial assessment of drug efficacy: “clinical activity”
– Screen out ineffective drugs
– Identify promising new drugs for future evaluation
• Further define safety and toxicity
– Type
– Frequency
• Provide sufficient evidence base to support Phase III development
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Outline
• Aims of a Phase II study
• Design aspects
– Disease/population selection
– Endpoint selection
– Single-arm vs randomised design
– Enrichment strategies involving biomarkers
– Statistical design aspects: Traditional, 2-stage vs adaptive/Bayesian
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Disease/population selection
• Aim to include patients who are most likely to benefit from the
intervention being tested; exclude patients unlikely to benefit or
at greater risk of harm
– Homogeneous population
One or more of the following factors may drive this
• A priori information
– Disease prevalence of a particular protein or gene abnormality
predicting for greater drug benefit based on drug MoA (biologic
rationale)
• Predictive or prognostic clinical factors or biomarker
– Pre-clinical evidence for activity/proof of concept in that tumour type
• Clinical factors
– Responses seen in Phase I
– Biologic rationale in disease area of unmet need
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Outline
• Aims of a Phase II study
• Design aspects
– Disease/population selection
– Endpoint selection
– Single-arm vs randomised design
– Enrichment strategies involving biomarkers
– Statistical design aspects: Traditional, 2-stage vs adaptive/Bayesian
• Case example
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Endpoint selection
• Response rate (RR)*
• Progression-free survival (PFS)*
• Overall survival (OS)
• Patient-reported outcomes (PRO)/ Quality of life (QoL)
– e.g., clinical benefit response of gemcitabine in pancreas CA (pain,
KPS, weight)
• Molecular biomarkers, e.g., biomarker expression
• Functional imaging, e.g., PET
• Toxicity
*assumes these are intermediate predictors for OS
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Primary endpoint (PE) selection
Danii et al. Clin Cancer Res. 2009;15(6)March15, 2009
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Primary endpoint selection
• RR vs PFS
– Requires understanding of expected drug effect on disease and
clinical setting, e.g., cytotoxic vs cytostatic; 1st line vs 2nd line vs
maintenance
• RR or PFS by RECIST
– Rigor depends on goal of the study, e.g., activity “screening” trial
(investigator review) vs decision-making “go/no-go” trial (independent
review)
– Cost implications with independent review, multiple scanning in short
intervals
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Primary endpoint selection RR vs PFS
• Know your drug
– What is drug expected to do?
– Is tumour response expected to occur based on drug MoA or prior
observation in Phase I?
• Expectations may differ with monotherapy vs combination with
chemo
• Decision may come down to purpose of the Phase II trial: ie,
“signal” finding vs “go/no-go”
– e.g., Phase II PFS endpoint for extension to Phase III OS study in
Phase II/III design OR
– RR endpoint for rapid screening in “pick-the-winner” randomised
Phase II study
• Study size depends on type I/II error rates around PE estimate
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Outline
• Aims of a Phase II study
• Design aspects
– Disease/population selection
– Endpoint selection
– Single arm vs randomised design
– Enrichment strategies involving biomarkers
– Statistical design aspects: Traditional, 2-stage vs adaptive/Bayesian
• Case example
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Types of Phase II studies
Seymour et al. CCR 2010 2010 March 15; 16(6): 1764–1769
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Single arm vs randomised design
Single Arm
Randomised
• Smaller sample size
• Generally larger
• Assumes some a priori information of
expected RR based on historical
information/control database
• Ideal for comparison of primary endpoint
OR for “calibration” against a control arm
where expected outcomes less certain
• One- or two-stage design
• Comparative or non-comparative
designs
• Efficient? short accrual esp. with RR but
only one study at a time
– More expensive but can explore multiple
arms at once
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Single arm vs randomised design: Primary endpoint RR
Monotherapy
Combination therapy
• Single-arm acceptable
• Randomised design usually preferred
esp. with new combinations
• Randomisation for testing different dose
or schedule or comparing with other
active therapies
– e.g., standard therapy ± novel agent, or
combination of novel agents
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Single arm vs randomised design: Primary endpoint PFS
Monotherapy
Combination therapy
• Single arm usually only acceptable when
there exists solid a priori info on expected
outcomes for patients/disease
• Randomised design most suitable
– Historical controls
– Database
• Use of placebo ideal (depending on
drug)
• Comparative vs
non-comparative designs
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Accuracy of single vs randomised Phase II studies
“Variability in historical control success rates, outcome drifts in patient
populations over time, and/or patient selection effects can result in
inaccurate false-positive and false-negative error rates in single-arm
designs, but leave performance of the randomised two-arm design
largely unaffected at the cost of 2 to 4 times the sample size
compared with single-arm designs.
Given a large enough patient pool, the randomised phase II designs
provide a more accurate decision for screening agents before phase III
testing”.
Tang H et al. J Clin Oncol 28:1936-1941
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Randomised Phase II designs: basic considerations
• Non-comparative
– Each arm considered on its own for the PE
– Good for multiple drug screening, “pick-the-winner” design
• Comparative
– Statistical design based PE in experimental arm compared with control arm
• Cross-over (after progression)
– To experimental therapy: to improve drug access, provide extra info
• Adaptive designs: evaluate patients' reactions to a drug early in a
clinical trial and modify the trial accordingly
– Adapt dose, target population; biomarker enrichment; futility criteria; sample
size re-estimation
• Randomised discontinuation
– Useful when enrolling patients in non-progressive disease setting with
cytostatic therapies
– A type of enrichment strategy
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Randomised discontinuation design
• Alternative phase II study design for determining activity of
”cytostatic” anticancer agents
• Example of enrichment design:
– To select a subset of enrolled patients, homogeneous with respect to
important prognostic factors, and randomise these
• Advantageous when a subset of patients, those expressing the
molecular target, is sensitive to the agent
Rosner GL et al. J Clin Oncol 2002;20: 4478-84; Freidlin B et al. J Clin Oncol 2005;23:5094-8.
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Randomised discontinuation design
Initial phase can be large 2nd phase (randomised phase); select patients thought most likely to benefit
Design (follow up) of each phase and sample size affected by
• Expected cancer growth rate, and
• Degree of drug activity
N = 335
Carboxyaminoimidazole (CAI, NSC 609974)
CALGB study in metastatic RCC.
Patients on placebo could cross over to CAI
PE: Proportion of patients progressing CAI vs placebo
This design for suspected cytostatic therapies where initial patient population may be too heterogeneous
Alternative designs: Simple randomised Phase II;
Placebo to agent for fixed periods; cross-over
Rosner GL et al. J Clin Oncol 2002; 20: 4478-84
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Outline
• Aims of a Phase II study
• Design aspects
– Disease/population selection
– Endpoint selection
– Single arm vs randomised design
– Enrichment strategies involving biomarkers
– Statistical design aspects: Traditional, 2-stage vs adaptive/Bayesian
• Case example
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Enrichment strategies: Population enrichment design
Learn Phase
Determine
Biomarker
Status
Conduct the
Trial of Study
Drug vs
Comparator
With Two
Biomarker
Cohorts
Confirm Phase
Biomarker
Positive
(BP)
Cohort
Biomarker
Negative
(BN)
Cohort
Assuming a Clear
Trend of Efficacy in
the BP Compared to
the BN Cohort, Conduct
the Trial of Study Drug
vs Comparator in the
BP Cohort onlya
Biomarker
Positive
Cohort
aIf
there are similar treatment effects in both cohorts, the entire population may be carried forward.
Ananthakrishnan R et al. Crit Rev Oncol Hematol. 2013 Oct;88(1):144-53
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Enrichment strategies
• Efficiency depends on strength of a priori information in relation to biomarker
• Known biomarker-effect relationship
– Go straight to biomarker-selected design
• e.g., ALK gene re-arrangement + Phase II population with ALK inhibitor
• Less certain correlation or broader action of drug beyond biomarker
– Include ALL; analyse by biomarker after N1, validate activity in biomarker selected
population in N2: e.g., PDL1 inhibitor studies
– Biomarker-selected first phase then unselected second phase T790M + NSCLC
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Adaptive designs
Berry DA. Nat Rev Clin Oncol. 2012 (9): 199-207
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Adaptive designs: Phase II-III
National Academy of Sciences, Nass, S, Harold L, Moses H, and Mendelsohn, J (Eds.), 2010. A National Cancer Clinical Trials System for the 21st
Century: Reinvigorating the NCI Cooperative Program. National Academies Press, Washington, DC.
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Outline
• Aims of a Phase II study
• Design aspects
– Disease/population selection
– Endpoint selection
– Single arm vs randomised design
– Enrichment strategies involving biomarkers
– Statistical design aspects: Traditional, 2-stage vs adaptive/Bayesian
• Case example
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Statistical design aspects
• Rule no 1: Involve a statistician early on!
• GLOSSARY
• P0 - the largest response proportion that, if true, means the treatment does not
warrant further study
• P1 –the smallest response proportion that, if true, implies that the treatment
warrants further study.
• Statistical test of hypothesis is then conducted to test the null hypothesis (H0):
• P ≤ P0 vs the alternative hypothesis that P ≥ P1, where
P is the true proportion responding to the treatment in the population
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Statistical design aspects
• Rule no 1: Involve a statistician early on!
• GLOSSARY
• α is the probability of rejecting the null hypothesis (H0) when it is true (Type I error – the incorrect
rejection of a true null hypothesis – false positive), usually 0.05 or 0.10 in Phase II trials
– i.e., the probability of rejecting the hypothesis that the proportion responding to the treatment is less than or equal
to P0 when this hypothesis is actually true,
Pr (Rejecting P≤P0/P≥P1)
• β is the probability of rejecting the alternative hypothesis when it is true (Type II error - failure to
reject a false null hypothesis – false negative)
– i.e., the probability of not rejecting the hypothesis that the proportion responding to the treatment is less than or
equal to P0 when this hypothesis is false, Pr (NOT Rejecting P≤P0/P≥P1)
• Power is the probability of rejecting the null hypothesis that the proportion responding to the
treatment is less than or equal to P0 when this hypothesis is false. Power = 1-β; usually 0.80 or 0.90
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Design considerations in Phase III trials
• Minimise cost of the trial
– Minimise number of patients exposed to an ineffective treatment
– Enroll as few patients as “necessary” to show benefit or failure
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Standard Single-Arm Phase II Study
• Binary endpoint (clinical response vs no response)
• Simple setup:
– α = 0.10, β = 0.10 (power = 0.90) H0 : P0 = 0.20 (null response rate) H1 : P1 = 0.40
(target response rate)
• Based on design parameters:
N = 36
– Conclude effective if 11 or more responses (i.e., observed response rate of ≥0.31)
• What 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 rule
– 2-stage designs
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Classic Simon 2-stage single-arm study
Ananthakrishnan R et al. Crit Rev Oncol Hematol. 2013 Oct;88(1):144-53
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Revised 2-stage design
• Stage 1: enroll 19 patients
– If 4 or more respond, proceed to stage 2
– If 3 or fewer respond, stop
• Stage 2: enroll 17 more patients (total N = 36)
– If 11 or more of total respond, conclude effective
– If 10 or fewer of total respond, conclude ineffective
• Design properties
• α = 0.10 H0 : p = 0.20 (null response rate) H1 : p = 0.40 (target response rate)
• What about power?
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2-Stage designs
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2-Stage designs
Gehan Two-Stage Design (1961)
• It is a two-stage design for estimating the response rate but providing for early
termination if the drug shows insufficient antitumour activity
• The design is most commonly used with a first stage of 14 patients. If no
responses are observed, the trial is terminated
Fleming Two-Stage Design (1982)
• Fleming’s design is the only two-stage design that we cover that may have the
early termination with the “accept the drug” conclusion
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Frequentist Versus Bayesian
• So far, “frequentist” approaches
• Frequentists: α and β errors
• Bayesians:
– Quantify designs with other properties
– General philosophy:
•
•
•
•
Start with prior information (“prior distribution”)
Observe data (“likelihood function”)
Combine prior and observed data to get “posterior distribution”
Make inferences based on posterior
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FDA criteria for adaptive Bayesian clinical trial
According to FDA guidelines, an adaptive Bayesian clinical trial can involve:
– Interim looks to stop or to adjust patient accrual
– Interim looks to assess stopping the trial early either for success, futility or harm
– Reversing the hypothesis of non-inferiority to superiority or vice versa
– Dropping arms or doses or adjusting doses
– Modification of the randomisation rate to increase the probability that a patient is
allocated to the most appropriate arm
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Adaptive randomisation designs
Begin assuming equally effective (1/3, 1/3, 1/3)
• May wait until a minimum number have been treated per arm
• Based on currently available (accumulated) data, randomise next patient (i.e.,
“weighted” randomisation)
• Stopping rules: drop an arm when there is “strong” evidence that
– It has low efficacy, OR
– It has lower efficacy than competing treatments
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Bayesian inferences
No p-values and confidence intervals
From the posterior distribution:
• Posterior probabilities
• Prediction intervals
• Credible intervals
Bayesian designs
• Can look at data as often as you like (!)
• Use information as it accumulates
• Make “what if” calculations
• Helps decide to stop now or not
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Biomarker – Integrated Approaches of Targeted Therapy for Lung
Cancer Elimination (BATTLE)
Umbrella protocol
Core needle biopsy
Biomarker profile
• EGFR mutation/copy number
• KRAS/BRAF mutation
• VEGF/VEGFR-2 expression
• RXRs/Cyclin D1 expression and
CCND1 copy number
Equal followed by
adaptive randomisation
Erlotinib
Vandetanib
Erlotinib +
bexarotene
Sorafenib
Kim ES et al. Cancer Discovery. Online April 3, 2011; DOI: 10.1158/2159-8274.CD-10-0010
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Biomarker – Integrated Approaches of Targeted Therapy for Lung
Cancer Elimination (BATTLE)
Kim ES et al. Cancer Discovery. Online April 3, 2011; DOI: 10.1158/2159-8274.CD-10-0010
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Biomarker – Integrated Approaches of Targeted Therapy for Lung
Cancer Elimination (BATTLE)
Kim ES et al. Cancer Discovery. Online April 3, 2011; DOI: 10.1158/2159-8274.CD-10-0010
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Key issues with adaptive designs
• Whether the adaptation process has led to design, analysis, or conduct flaws
that have introduced bias that increases the chance of a false conclusion that the
treatment is effective (a Type I error)
• Whether the adaptation process has led to positive study results that are difficult
to interpret irrespective of having control of Type I error
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Outline
• Aims of a Phase II study
• Design aspects
– Disease/population selection
– Endpoint selection
– Single-arm vs randomised design
– Enrichment strategies involving biomarkers
– Statistical design aspects: Traditional, 2-stage vs adaptive/Bayesian
• Case example
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Case Example: INTEGRATE Study
J Clin Oncol 33, 2015 (suppl 3; abstr 9)
• The disease and patient population
• 2012: The treatment of refractory advanced oesophago-gastric cancer (AOGC) remains
an area of unmet need
• Vascular endothelial growth factor (VEGF) associated with prognosis in AOGC
– High IHC expression: poor outcome
– Low sVEGF: better outcome
– 1st-L PIII AVAGAST study: Chemotherapy (CX) +/- bevacizumab: improved PFS; ORR but not OS
• The drug:
• Regorafenib (BAY 73-4506) - oral multi-kinase inhibitor targeting angiogenic (VEGFR,
TIE-2), stromal (PDGFR-β) and oncogenic (RAF, RET and KIT) receptor tyrosine kinases
– Effective in Phase III studies in refractory colorectal cancer (CORRECT study); and refractory
GIST (GRID study)
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Case Example: INTEGRATE Study
J Clin Oncol 33, 2015 (suppl 3; abstr 9)
• The study question: Is regorafenib active in AOGC?
– Goal: to determine if sufficient activity for Phase III study (screening)
• Study design and considerations
– Phase II, multicentre study
• Single-arm vs randomised?
– Concerns: “cytostatic” drug (mainly SD in CORRECT study); uncertain PFS in AOGC
• Randomised Phase II: placebo-controlled
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Case Example: INTEGRATE Study
J Clin Oncol 33, 2015 (suppl 3; abstr 9)
• Study population
• Adults with AOGC, histo/cytologically confirmed (adeno or undifferentiated)
refractory to 1st- or 2nd-line chemo
– No prior anti-VEGF therapy
• Type of disease: Measurable or evaluable?
• Primary endpoint selection: ORR vs PFS
• PFS
– Measurable disease defined by RECIST Version 1.1 by CT scan performed within 21
days prior to randomisation
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Case Example: INTEGRATE Study
J Clin Oncol 33, 2015 (suppl 3; abstr 9)
• Study design questions
• Why have a control arm?
• Comparative vs non-comparative design?
– Both are option: N greater in comparative design
• Interim analysis?
• Randomisation: 1:1 vs 2 (Regorafenib): 1 (placebo)
• Cross-over: Y or N?
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Case Example: INTEGRATE Study
J Clin Oncol 33, 2015 (suppl 3; abstr 9)
STATISTICAL PLAN
• Assumption for this patient population:
– Median TTP approximately 2 months. An increase in the median TTP to 3.33 months for patients
receiving regorafenib will be of clinical interest.
• The primary endpoint is the median progression-free survival (PFS) in the
intervention group.
• N = 92 patients in the treatment group will have 90% power with 95% confidence to
include a clinically interesting 66% PFS at 3 months and exclude a less interesting 50%
PFS rate at 3 months. 100 patients will be randomised to allow for drop out/ineligibility.
• Simon 2-stage design
• Futility analysis after 33 patients have been followed for at least 2 months. If 15 or more
patients have not progressed then the study regimen/design will be reassessed or the
study stopped.
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Case Example: INTEGRATE Study
J Clin Oncol 33, 2015 (suppl 3; abstr 9)
• Randomised phase II calibration design
• N=100 regorafenib (active arm)
– Simon 2-stage design
– Null hypothesis (H0): 2 month PFS ≤50% (i.e., median PFS ≤2.00 months)
– Alternative hypothesis (HA): 2 month PFS ≥66% (i.e., median PFS
≥3.33 months)
– N=100 provides >90% power at 5% significance to reject H0 if HA is true
• N=50 placebo (calibration arm)
– To judge the applicability of the reference hypothesis (H0)
– To provide a reference if H0 found not applicable
• A total of N=150 patients randomised 2:1 stratified by prior chemotherapy lines (1 vs 2)
and geographic region (ANZ/Canada/Korea)
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CONCLUSION
PHASE II TRIAL DESIGNS
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Appropriate Primary Endpoint
“Response”
Tumour shrinkage expected
(or other qualified biomarker)
PFS
Combination
Monotherapy
Randomised
design
Only if
robust
control
available
Consider
Single arm design
Randomised
design
Single
arm
design
• Include secondary endpoints (biomarkers, PROs, imaging)
• Biomarkers
̶ Do not enrich unless clinically validated
̶ Consider adaptive designs
̶ Consider multi-disease trials
Seymour et al. CCR 2010 2010 March 15; 16(6): 1764–1769
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