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

2East 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


• 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?

April 23-24, 2013 East Webinar 5

• 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


Normal Two-Sample


• 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


Input Window


Inputs with Output Preview


Use of filter to eliminate designs


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


View the Boundaries and Compute

With futility boundary, sample size has increased to 625 patients


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


Compare Design Summaries


Incorporate Delayed Response and Dropouts


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


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


Examine Final Design in Detail

Icons for viewing design details, graphs, tables and summaries


• 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


• 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


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


Demonstrating 1.8 non-inferiority

Could file for 1.8 non‐inferiority within 17 months


Comparing E-charts for 1.3NI and 1.8NI


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


Investigate by simulation

Impact of Lower Event Rate on 1.3NI


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


• 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


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


Add the stratification information


Compare Stratified and Unstratified


• 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


R-function for Weibull Distribution


Compare designs with Weibull Responses


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


Thank You

Questions?


cyrus mehta, ph.d. president, cytel inc. · 2017. 10. 8. · • primary outcome is major adverse...

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