1. Utilizing of Platform Clinical Trial to Help Make Faster Decisions
ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop
J. Kyle Wathen
Director
Statistical Modeling and Methodology

2. Outline Very brief introduction to platform trials
Bayesian decision framework
Package website
Future plans

3. Platform Trial - Introduction
An experimental infrastructure to evaluate multiple treatments and/or combinations of treatments in heterogeneous patient populations.
Not all interventions are included, or even known, at the start of the platform
Utilize a Master Protocol – no compound specifics
New compounds are added with an Intervention Specific Appendix (ISA) - details about the compound
Key benefits include – improvements in patient recruitment and borrowing of patient data across ISAs

4. Typical Timeline - POC Jan 2020 Start Platform Jan 2022 Jan 2024
Compound 5
Compound 4
Compound 3
Compound 2
Compound 1
Jan 2020
Start Platform
Jan 2022
Jan 2024
Jan 2026

5. Sharing Information Between ISAs
Start Platform 5

6. Sharing Information Between ISAs
Start Platform 6

7. Sharing Information Between ISAs
Start Platform 7

8. Sharing Information Between ISAs
Start Platform 8

9. Design and Simulation Many similarities between ISAs
Each ISA may require different designs, eg single vs multiple doses
One ISA can impact another
Recruitment
Placebo/Control sharing
ISAs may be concurrent or consecutive
Currently no commercial software or package that can accomplish all of the requirements
Started with R code for first platform trial then extended and started a package in R for the second platform

10. Bayesian Go-No Go Decision Rules
Dual Criteria for each Outcome
MAV – Minimum Acceptable Value, TV Target Value
mP, mE parameter of interest for P and E, respectively
Calculations
Pr( d = mP - mE > MAV | data ) > LB
Pr(d = mP - mE > TV| data ) > UB
Cutoffs: LB, UB
Pr(d > MAV | data ) ≥ LB
Pr(d > MAV | data ) < LB
Pr(d > TV | data ) ≥ UB
Graduate
Indeterminate/Continue
Pr(d > TV | data ) < UB
Drop

11. Pr(d > MAV | data ) < 0.9
Decision Making
Pr( d > MAV | Data )
Pr( d > TV | Data )
Pr(d > MAV | data ) ≥ 0.9
Pr(d > MAV | data ) < 0.9
Pr(d > TV | data ) ≥ 0.05
Graduate
Indeterminate/Continue
Pr(d > TV | data ) < 0.05
Drop

12. Combining Outcomes Design 1 Design 2 Outcome 1 Outcome 1 Outcome 2
Graduate
Continue
Drop
Graduate
Continue
Drop
Outcome 2
Outcome 2

13. Why R Package for Platform Simulation?
OCTOPUS – Optimize Clinical Trials on Platforms Using Simulation
Not all features are available commercially –desire to test and reuse work if possible
Having the R source code allows for new options and extensions
Transparent to the nature of what is going on in the trial
Many statisticians are familiar with R
Freely available at

14. OCTOPUS - Key Features Random or fixed entry times for ISA
Each ISA can have different modeling and decisions
Information sharing across ISAs, each ISA can be different
Monitoring of ISA can be setup as minimum information with subsequent analysis at predetermined interval or the amount of information needed for each interim analysis
Flexibility to add new patient simulator, analysis, randomizers, ect
Any number of outcomes
Some default plots can be created
Covariates and subgroup specific decisions is in development

15. R Package + Project Specific Files
Core components
Built on generic functions
Tested
Generalized functions from projects
Community driven development in future versions
Define trial design element
Define simulation design element
Define any project specific functions
Key Advantages – Tested code, reuse general parts, speed up development, learn across projects, project details remain in the project specific files, extendable, generic concepts can be moved from projects to package

17. Shiny App Compare Recruitment
Quickly compare options of 2 POC vs Platform with 2 ISAs in terms of recruitment
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20. Conclusion and Future Plans
Have had positive feedback from several people on wanting to use the package
Have utilized the package to simulate more than 5 platform studies and 3 non-platform
Developers guide
Task list
Streamline the simulation and creation of results for “standard” output
Training
In person and videos
Training for someone use the package and for anyone interested in helping to extend the package

21. Thank You! Kyle Wathen kwathen@ITS.JNJ.COM

22. Backup