Contains three simulation functions for implementing the entire Phase 123 trial and the separate Eff-Tox and Phase 3 portions of the trial, which may be beneficial for use on clusters. The functions AssignEffTox() and RandomizeEffTox() assign doses to patient cohorts during phase 12 and Reoptimize() determines the optimal dose to continue with during Phase 3. The functions ReturnMeansAgent() and ReturnMeanControl() gives the true mean survival for the agent doses and control and ReturnOCS() gives the operating characteristics of the design.

Simulating and conducting four phase 12 clinical trials with correlated binary bivariate outcomes described. Uses the 'Efftox' (efficacy and toxicity tradeoff, <https://biostatistics.mdanderson.org/SoftwareDownload/SingleSoftware/Index/2>) and SPSO (Semi-Parametric Stochastic Ordering) models with Utility and Desirability based objective functions for dose finding.

Implements Bayesian phase I repeated measurement design that 
    accounts for multidimensional toxicity endpoints and longitudinal efficacy 
    measure from multiple treatment cycles. The package provides flags to 
    fit a variety of model-based phase I design, including 1 stage models with
    or without individualized dose modification, 3-stage models with or without
    individualized dose modification, etc. Functions are provided to recommend 
    dosage selection based on the data collected in the available patient cohorts 
    and to simulate trial characteristics given design parameters. 
    Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.

Implements our Bayesian phase I repeated measurement design that accounts for multidimensional toxicity endpoints from multiple treatment cycles. The package also provides a novel design to account for both multidimensional toxicity endpoints and early-stage efficacy endpoints in the phase I design. For both designs, functions are provided to recommend the next dosage selection based on the data collected in the available patient cohorts and to simulate trial characteristics given design parameters. Yin, Jun, et al. (2017) <doi:10.1002/sim.7134>.