SimMABOUST {MABOUST}

Simulate the MABOUST Trial design.

Description

Simulates trial replicates of the MABOUST trial and reports Operating Characteristics (OCs).

Usage

SimMABOUST(
  nSims,
  NLOOK,
  nTreat,
  nCat,
  UT,
  DeltaVEC,
  gamma,
  PSPIKE,
  ADJ,
  B,
  PROBS,
  Beta,
  XPROB
)

Arguments

nSims

Number of trial replications to complete.

NLOOK

Vector containing how many patients should be evaluated before each interim decision.

nTreat

Number of treatments in consideration, i.e. K.

nCat

Number of ordinal outcome categories, i.e. J.

UT

Vector of numerical utility scores to give outcomes 1,...,J.

DeltaVEC

Vector of \bf{\Delta} values to test.

gamma

Length 3 vector of cutoff parameters.

PSPIKE

Prior probability of a pairwise null effect.

ADJ

Binary indicator of whether covariate adjustment is used.

B

Number of MCMC iterations to perform.

PROBS

K-list of J-vectors containing ordinal outcome probabilities.

Beta

Covariate Effect Vector on Outcome.

XPROB

List of matrices containing discrete values various covariates can take, along with their probabilities.

Value

The set of active treatments to continue, an optimal treatment, or a set of equally optimal treatments. Also reports posterior mean utilities and ordinal outcome probabilities as well as pairwise comparisons of utility similarity, when appropriate.

References

Chapple, A.G., Bennani, Y., Clement, M. (2020). "MABOUST: A Multi-Armed Bayesian Ordinal Outcome Utility-Based Sequential Trial". Submitted.

Examples

##Clinical Parameters
nCat = 6
nTreat = 3
UT = c(0,10,20,80,90,100)  ###Utilities
DeltaVEC  = c(5,10)   ###Vector of deltas to try
NLOOK = c(20,50)  ###Interim Looks
###Which treatments are active?
ACTIVE = c(1,0,1) ###Treatments 1, 3 are active
FUTILITY = 1 ###Futility look is allowed.
###Design parameters
gamma= c(.5, .05, .05)
PSPIKE = .9
set.seed(1)
##Generate Random Data
n=300
Y=sample(1:nCat,n,replace=TRUE)
T1 = sample(1:nTreat,n,replace=TRUE)
XPROB = as.list(rep(NA,3))
XPROB[[1]]=rbind(0:10,round(dpois(0:10,2),2)) ###CCI
XPROB[[2]]=rbind(c(-1,0,1),c(.5,.4,.1)) ###O2 Status
XPROB[[3]]=rbind(c(-2,-1,0,1),c(.27,.38,.18,.17))
Beta =
###Number of iterations
B=100
##Get Simulation Parameters
 #' ##Get Simulation Parameters
 PROBS = as.list(rep(NA,3))
 PROBS[[1]]=c(.33,.11,.42,.02,.11,.01)
 PROBS[[2]]=c(.24,.11,.48,.05,.11,.01)
 PROBS[[3]]=c(.14, .20, .48, .03, .12, .03)
 Beta=c(-.13, -.07, -.10)
 nSims=1 ##Number of sims to run
 ADJ=1
 SimMABOUST(nSims,NLOOK, nTreat,nCat, UT, DeltaVEC,gamma,PSPIKE,ADJ, B, PROBS, Beta, XPROB)