Cyrus Mehta, Ph.D. President, Cytel Inc. · 2017. 10. 8. · • Primary outcome is major adverse...
Transcript of Cyrus Mehta, Ph.D. President, Cytel Inc. · 2017. 10. 8. · • Primary outcome is major adverse...
Introduction to the new East 6on the Architect Platform
Cyrus Mehta, Ph.D.President, Cytel Inc.
Agenda
• Introduction to the new East on the Architect platform• Four Examples to Illustrate New Features of East
1. Schizophrenia Trial: illustrates more flexible boundaries; delayed response; incorporating Bayesian priors into design
2. Cardiovascular Outcomes Trial: illustrates non‐inferiority based on FDA guidance provisional approval based on surrogate and final approval based on clinical endpoint
3. Lung Cancer Trial: illustrates the impact of stratified randomization on study design
4. Calls to R functions within simulations• Q & A
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3East WebinarApril 23-24, 2013
1994 2000 2010
Where are we today?
• East on the Architect platform is backed up by 20 years of R&D
• Its algorithms have been thoroughly battle‐tested
• It is the industry standard for designing adequate and well‐controlled clinical trials
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• Broad coverage of designs for biostatisticians• One integrated tool for all types of designs: fixed sample, group sequential or adaptive
• Superior User Interface• Multiple windows with graphs and tables • Organized storage of designs in workbooks
• Rapid creation, viewing and filtering of multiple scenarios for design parameters
• Commitment to continuous improvement and expansion of features
What is special about the new East?
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• Integrates East and SiZ in a common interface• Handles response lags and drop‐outs for normal and binomial endpoints
• Greater flexibility with futility boundaries and display on different scales
• Simulates stratified randomization designs• Permits external calls to R within simulation cycle
• Incorporates uncertainty about and through Bayesian priors for power calculations
New Statistical Capabilities
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Normal Two-Sample
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• Two arm trial: asenapine vs. olanzapine (control)• Primary endpoint is negative symptoms assessment (NSA) on 24‐point scale observed at week 26
• Design for = 2 and = 7.5 with two sided =0.05 and 80% power
• But could be as small as 1.6 so examine a range of sample size requirements
• Enrollment ramps up to 8 patients/week• Up to 8% drop‐outs anticipated
Example 1: Schizophrenia Trial
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Input Window
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Inputs with Output Preview
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Use of filter to eliminate designs
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Convert Single Look Design into GSD
First save the selected design(s) to the library for further editing
Then change number of looks and boundary information
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View the Boundaries and Compute
With futility boundary, sample size has increased to 625 patients
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Bayesian Probability of Success (Assurance)
Assign a prior distribution to to capture the uncertainty associated with it
The “Assurarce” of success is only 0.685 due to uncertainty about
In this case the “Assurance is 0.798 dueto less uncertainty about
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Compare Design Summaries
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Incorporate Delayed Response and Dropouts
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Compare designs with and without lag
• Notice that, due to the 26 week response lag, there is very little saving in expected sample size.
• Maximum study duration is 110 weeks
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Reason for lack of sample size saving in GSD when there is a long delay in the primary response
• The is a large gap (> 208 patients) between sample size and completers• By the time 669 cpmpleters for IA 3 the total enrollment is over. Hence
no saving of sample size if trial stops at look 3• But, there is a saving of time; trial stops 20 weeks earlier if stopped at look 3
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Examine Final Design in Detail
Icons for viewing design details, graphs, tables and summaries
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• New agents for type‐2 diabetes must demonstrate safety to cardiovascular risk
• Primary outcome is major adverse cardiac event (MACE) ‐‐ death, MI, stroke
• =0.025 ‐‐ if upper bound of 95% CI for HR is:• < 1.8 – file for provisional approval• < 1.3 – file for final approval
• 2.5% annual event rate in this population• Plan 24 month enrollment; 60 month study
Example 2: CV Outcomes Trial
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• How many patients are needed for a 5‐year study?
• What is the impact of drop‐outs and non‐uniform accrual?
• How soon can we demonstrate non‐inferiority to HR=1.8?
• What is the impact of population heterogeneity on study duration and sample size?
Important Design Questions
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Sample size requirements incorporating drop-outs and non-uniform accrual
• 611 events are needed for 1.3 non‐inferiority
• Sample size is 6844 for a 5‐year study
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Demonstrating 1.8 non-inferiority
Could file for 1.8 non‐inferiority within 17 months
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Comparing E-charts for 1.3NI and 1.8NI
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Suppose the annual event rate is 2% instead of 2.5%. What is the impact on 1.8 NI submission?
Impact of Lower Event Rate on 1.8 NI
The interim and final analysis times are each delayed by about 2
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Investigate by simulation
Impact of Lower Event Rate on 1.3NI
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12 month delay in testing 1.3 NI
If event rate is 2.5%/year, the study duration is 60 monthsIf event rate is 2%/year, the study duration is 72 months
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• The base‐case design has:• 2‐sided level 0.05 logrank test• 90% power to detect HR = 0.447• Baseline hazard (control arm) = 0.009211• 3 equally spaced OBF stopping boundaries• Uniform enrollment at 12/weeks for 24 weeks
• Consider stratification by cell type (4), age (2) and performance status (2)
Example 3: Lung Cancer
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Kalbfleisch and Prentice (2002) Data
Cell type Proportion Hazard Ratio
Small cell 0.28 Baseline
Adenocarcinoma 0.13 2.127
Large cell 0.25 0.528
Squamous 0.34 0.413
Age Group Proportion Hazard Ratio
≤ 50 0.28 Baseline
> 50 0.72 0.438
Karnofsky P.S. Proportion Hazard Ratio
≤ 50 0.43 Baseline
50 to 70 0.37 0.164
>70 0.20 0.159
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Add the stratification information
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Compare Stratified and Unstratified
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• Base Case: single look design; 1‐sided = 0.025and default values for other parameters
• Simulate design without any modification• Simulate design with responses generated by a Weibull distribution called up from R
• With shape parameter > 1: decreasing hazard• With shape parameter 1: constant hazard• With shape parameter <1: increasing hazard
Example 4: Calls to External R-routines
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R-function for Weibull Distribution
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Compare designs with Weibull Responses
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East 6.1•Assurance•Stratified Logrank•R connection
East 6.2•Unblinded SSR•Negative binomial/Poisson
•Interval‐based designs
•Gatekeeping procedures for Multiple endpoints
East 6.3•Two‐stage adaptive dose selection
•Blinded SSR•Predictive interval plots
Roadmap to further development
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Thank You
Questions?
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