1. Utilizing Bayesian Analysis for Probabilistic Decision Making in a Platform Clinical Trial
Joint Statistical Meeting 2019
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?
Not all features are available commercially –desire to test and reuse work if possible
Many similarities across platforms but also many differences
Each project has added functionality to the package and expanded it
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

14. R Package 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
Image or graphic goes here

18. Image or graphic goes here

19. Community Driven Software
ASA Biopharmaceutical Software Work Group
Formed at the 2018 JSM
Alex Dmitrienko (Chair), Kyle Wathen (Vice chair) representatives from 10 pharmaceutical companies and RA
Advance the development of community software
Have been given great input/guidance from members of this group
Package naming survey – let the community be involved and help name and pick the ICON for the project
If interested in voting please let me know or connect with me on LinkedIn

21. Future Plans Naming survey Developers guide Task list
ViPER – Virtual Platform Evaluator in R
ViTalS – Virtual Trial Simulator
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

22. Backup

23. GitHub Source control – GitHub
Package website on GitHub built with pkgdown
Test code
Utilizing testthat currently > 800 tests
Test coverage provided by codecov – currently 47%
Automated build process is passing
Utilized Travis CI