Utilizing Bayesian Analysis for Probabilistic Decision Making in a Platform Clinical Trial Joint Statistical Meeting 2019 J. Kyle Wathen Director Statistical Modeling and Methodology

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

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

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

Sharing Information Between ISAs Start Platform 5

Sharing Information Between ISAs Start Platform 6

Sharing Information Between ISAs Start Platform 7

Sharing Information Between ISAs Start Platform 8

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

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

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

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

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

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

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

Shiny App Compare Recruitment Quickly compare options of 2 POC vs Platform with 2 ISAs in terms of recruitment Image or graphic goes here

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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

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

Backup

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