east-escalate user workshop - cytel
TRANSCRIPT
East-Escalate User Workshop®
JSM 2015, Seattle
Hrishikesh Kulkarni, Charles Liu
Aug, 2015
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Outline
1 Phase 1 Dose Finding
2 References
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Phase 1 Dose Finding
Sequence of K fixed doses: d1, d2, ...dK
Each dose i has toxicity probability pi
Monotonicity assumption
Maximum Sample Size, cohort size, starting dose
Method for sequentially assigning doses to cohorts
Find MTD: highest dose with toxicity less than target pT
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Dose Assignment Rules
In Phase 1 trial designs, why not assign patients equally at each dose?
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Ethical Requirement
”The major difficulty in phase I trial design and conduct is the ethicalrequirement that the number of patients in the trial who experiencetoxicity must be limited.”
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Phase I Trial Challenges
Under-dosing (risk of absence of any anti-tumour activity).
Over-dosing (risk of severe toxicity).
Minimize sample size (unproven agent).
Maximize information (on toxicity and PK profile).
Paoletti et al. (2006). European Journal of Cancer, 42(10), 1362–8.
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Bayesian
Prior: uninformative, or published literature / experts
Likelihood: observed data
Posterior: updated evidence
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Rule-based vs Model-based
Rule-based / Algorithmic (eg., 3+3) vs Model-based (CRM, BLRM)
mTPI is a mixture of both
Why model-based?
more flexible (eg., different cohort sizes)Bayesian statistical inferencesBut, more complex (need for user-friendly software!)
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3+3 (Prevalence)
Over 98% of published Phase 1 trials (1991-2006) use variations of 3+3
(Rotgako et al., 2007)
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3+3 (Prevalence)
Over 96% of published Phase 1 trials (2007-2008) use variations of 3+3
(LeTourneau et al., 2009)
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East ESCALATE
http://www.youtube.com/watch?v=6txAE3eGOk8
Two modes: (1) Simulation; (2) Interim Monitoring
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3 + 3 H (implicit pT = 1/3)
http://www.mdanderson.org/education-and-research/departments-programs-and-labs/departments-and-divisions/division-of-quantitative-sciences/lectures-and-seminars/peter-f.-thall-presentation.pdf
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3 + 3 L (more common, implicit pT = 1/6)
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Subject-wise Dose Allocation
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Limitations of 3+3
Alessandro Matano, Novartis, http://www.smi-online.co.uk/pharmaceuticals/archive/4-2013/conference/adaptive-designs
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Target Toxicity?
Common misconception target toxicity is fixed (eg., 17%, or 33%).
He et al. (2006) showed via simulation that the expected toxicity level at theMTD for the 3+3 is between 19-22%.
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Workshop Exercise (Simulation)
Adapted from Thall & Lee (2003):
”Patients with renal cell carcinoma (RCC) that was progressive after previoustreatment with interferon were eligible. Treatment consisted of fixed doses of5-FU and interferon, plus one of six doses of gemcitabine (GEM): 100, 200,300, 400, 500, or 600 mg/m2. Toxicity was defined as grade 3 or 4 diarrhea,mucositis, or hematological toxicity. A total of 36 patients were treated incohorts of size 3, with the first cohort given 200 mg/m2 of GEM.”
Enter relevant inputs into East
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Output Details
For each scenario...
1 ...which dose is selected most often (and how often) as the MTD by 3+3?
2 ...how often does 3+3 fail to select an MTD?
3 ...what are the median and mean sample sizes?
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modified Toxicity Probability Interval (mTPI)
mTPI is rule-based like 3+3 but Bayesian like CRM and BLRM
Challenges for model-based methods: complexity (esp for non-statisticians);sensitivity to priors
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modified Toxicity Probability Interval (mTPI)
“...almost all phase I oncology trials conducted at Merck in past 2 years havebeen based on the mTPI design”
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modified Toxicity Probability Interval (mTPI)
Probability of toxicity at each dose modeled by independent Beta distributions
Set of decision intervals specified (like in BLRM)
Dosing decisions determined by ’normalized’ posterior probability in eachinterval at the current dose di :
Escalate to di+1 if di is ’underdosing’Stay at di if ’proper dosing’De-escalate to di−1 if di is ’overdosing’
Compute UPM for each interval. The one with largest UPM implies thedecision.
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mTPI Priors
“[W]e believe that for phase I trials with small sample sizes...the dependenceintroduced by prior models will have a strong influence on the operatingcharacteristics...The independent prior models performs quite well comparedto existing approaches.”
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Equivalence Intervals
The Equivalence Interval (EI) is defined as [pT − ε1, pT + ε2]
pT − ε1 is the lowest toxicity probability that the physician would becomfortable using to treat future patients without dose escalation
pT + ε2 is the highest toxicity probability that the physician would becomfortable using to treat future patients without dose de-escalation
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Unit Probability Mass
UPM(interval) = Post Pr(interval) / length(interval)
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Trial Monitoring Table (for Clinicians)
E=Escalate, S=Stay, D=De-escalate, DU=De-escalate & Unacceptable
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mTPI Dose Exclusion / Stopping Rule
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Regulatory Guidelines
FDA Guidance (Clinical Considerations for Therapeutic Cancer Vaccines)
EMEA / CHMP Guideline on Clinical Trials in Small Populations
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Continual Reassessment Method (CRM)
Bayesian model-based method (O’Quigley et al. 1990)
Uses all available information from doses to guide assignment
Inputs to specify:
target toxicity pT (e.g., 0.3)one-parameter (θ) dose-toxicity curveprior distribution for θprior mean probabilities at each dose (“skeleton”)
Next recommended dose: posterior toxicity probability closest to target
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Modified CRM
Differences:
Can start at lowest doseAllow multiple patients per cohortRestrict escalation to one dose (do not allow skipping when escalating)
“The unmodified CRM...produces only modest increases in accuracy overthe modified CRM, but at the price of greater toxicity, and, mostimportant, clinical acceptability.”
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CRM Dose Toxicity Curves & Priors
Logistic: p(di ) = expc+diθ
1+expc+diθ, c fixed
Hyperbolic Tangent: p(di ) =(
tan(di )+12
)θ
Power: p(di ) = (pi )θ
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Bayesian Logistic Regression Model (BLRM)
Two-parameter logistic: logit(p(di )) = logα + β log(di/d∗)
d∗ is the “reference dose”: logit(p(d∗)) = logα.
The approach is recommending a range of doses instead of just one dose.
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Uncertainty in toxicity rate
CRM relies on point estimate (posterior mean) , ignores uncertainty andcould be misleading.BLRM uses entire posterior distribution at each dose.eg, Two beta distributions with same posterior mean but very differentvariances. Pr(Overdosing), Pr(p > 0.6) = 0.168 vs 0.002
X-axis - probability of observing DLT
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Escalation With Overdose Control (EWOC)
Choose dose that maximizes targeted toxicity probability, given notoverdosing.
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Bayes Risk
Choose dose that minimizes posterior expected loss.
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Prior Specification (direct vs indirect)
Enter directly bivariate normal for log(α) and log(β) OR
Enter “best guess” for p(d1) and MTD
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Prior Specification (indirect)
1 Assuming logits of toxicity are linear, calculate prior probabilities of toxicity(predicted median) at each dose level
2 Assign a “minimally informative unimodal” Beta distribution at each doselevel (Neuenschwander et al., 2008 Appendix A)
3 Generate n sets of logits from Beta distributions, to obtain n estimates oflog(α) and log(β) using least squares
4 Use sample means, variance, correlation for bivariate normal
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Posterior Sampling Methods
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Interval Probabilities by Dose
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BLRM Example
First in-Human study on advanced solid tumors.Primary endpoint - frequency of DLTDoses - 1, 2 , 4, 8, 15, 30, 40.Weak Prior Informationd* set to 10BVN prior for log(α) and log(β)
means = (-0.693, 0), sd = (2, 1), corr = 0
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Workshop Exercise (Interim Monitoring)
Adapted from Neuenschwander (2008):
”This study is an open-label, multicenter, non-comparative, dose-escalationcancer trial designed to characterize the safety, tolerability, andpharmacokinetic profile of a drug and to determine its MTD. The pre-defineddoses were 1, 2.5, 5, 10, 15, 20, 25, 30, 40, 50, 75, 100, 150, 200 and 250mg.”
“Currently in our trials the dose recommendation relies on maximizing theprobability of target toxicity while controlling the probability of excessive orunacceptable toxicity at 25 per cent.”
Enter relevant inputs into East
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Simulation Parameters
”The best matching bivariate normal prior for the two-parameter logisticmodel had parameters µ1 = 2.15, µ2 = 0.52, σ1 = 0.84, σ2 = 0.8, ρ = 0.2”
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Simulation Stage Plots in Library
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Interim Monitoring
“It was decided....not to skip dose levels during dose escalation.”
“The first cohort of patients was treated at 1 mg. No DLTs were observed forthe first four cohorts of patients, and the clinical team then decided to skiptwo dose levels...At 25 mg, two DLTs were seen in two patients. The datafrom the fifth cohort led to discussion about the dose for the next cohort.”
Enter these data into East. On the basis of information from the InterimMonitoring Dashboard, what dose would you recommend next?
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Final Inference
“Eventually, the trial was continued with a dose of 20 mg, a total of ninepatients were enrolled at that dose, and two DLTs occurred. Then the trialwas stopped and 20 mg was declared as the MTD.
Enter and click “Final Inference”
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Summary
Almost all trials to date have used rule-based methods
Rule-based methods (eg., 3+3) are easy to implement and simple to explain
Model-based methods (eg., CRM, BLRM) are more flexible and efficient, butperformance may depend on prior information
mTPI may be a useful compromise
East ESCALATE provides two modes:
1 Simulations for comparing and evaluating designs2 Interim Monitoring for executing designs and analyzing data
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Work in Progress
Combination Designs
Enhancements
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Outline
1 Phase 1 Dose Finding
2 References
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References
Neuenschwander, Branson, Gsponer. Critical aspects of the Bayesian approach to phase I cancer trials.Statistics in Medicine 2008;27:2420 - 2439
Ji, Wang. Modified Toxicity Probability Interval Design: A Safer and More Reliable Method Than the 33 Design for Practical Phase I Trials. Journal of Clinical Oncology, 2013
Thall, Lee. Practical model-based dose-finding in phase I clinical trials: Methods based on toxicity. Int JGynecol Cancer 2003, 13, 251 - 261
He, Liu, Binkowitz, Quan. A model-based approach in the estimation of the maximum tolerated dose inphase I cancer clinical trials. Statistics in Medicine 2006; 25:2027 - 2042
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