The goal of bmabasket is to simulate basket trial data based on hyperparameters and analyze things such as the family-wise error rate, bias, and MSE. The package uses Bayesian model average (BMA) to compute the posterior probability that response within a basket exceeds some threshold.

## Installation

You can install the released version of bmabasket from CRAN with:

``install.packages("bmabasket")``

And the development version from GitHub with:

``````# install.packages("devtools")

## Example

This is a basic example which shows you how to solve a common problem:

``````library(bmabasket)
## REPEAT SIMS FROM BIOSTATISTICS JOURNAL PUBLICATION
nSims      <- 100             ## change to ~250000 to repeat journal results
meanTime   <- 0.01
sdTime     <- 0.0000000001
mu0        <- 0.45
phi0       <- 1.00
ppEffCrit  <- 0.985
ppFutCrit  <- 0.2750
pmp0       <- 2
n1         <- 7
n2         <- 16
targSSPer  <- c(n1, n2)
nInterim   <- 2
futOnly    <- 1
K0         <- 5
row        <- 0
mss        <- 4
minSSFut   <- mss  ## minimum number of subjects in basket to assess futility using BMA
minSSEff   <- mss  ## minimum number of subjects in basket to assess activity using BMA
rTarg      <- 0.45
rNull      <- 0.15
rRatesMod  <- matrix(rNull,(K0+1)+3,K0)
rRatesNull <- rep(rNull,K0)
rRatesMid  <- rep(rTarg,K0)
eRatesMod  <- rep(1, K0)

## min and max #' of new subjects per basket before next analysis (each row is interim)
minSSEnr <- matrix(rep(mss, K0), nrow=nInterim ,ncol=K0, byrow=TRUE)
maxSSEnr <- matrix(rep(100, K0), nrow=nInterim, ncol=K0, byrow=TRUE)

## construct matrix of rates
for (i in 1:K0)
{
rRatesMod[(i+1):(K0+1),i]= rTarg
}
rRatesMod[(K0+2),] <- c(0.05,0.15,0.25,0.35,0.45)
rRatesMod[(K0+3),] <- c(0.15,0.30,0.30,0.30,0.45)
rRatesMod[(K0+4),] <- c(0.15,0.15,0.30,0.30,0.30)

## conduct simulation of trial data and analysis
x <- bma_design(
nSims, K0, K0, eRatesMod, rRatesMod[i+1,], meanTime, sdTime,
ppEffCrit, ppFutCrit, as.logical(futOnly), rRatesNull, rRatesMid,
minSSFut, minSSEff, minSSEnr, maxSSEnr, targSSPer, nInterim, mu0,
phi0, priorModelProbs = NULL, pmp0 = pmp0
)
x
#> \$hypothesis.testing
#> \$hypothesis.testing\$rr
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.92 0.85 0.86 0.87 0.84
#>
#> \$hypothesis.testing\$fw.fpr
#> [1] 0
#>
#> \$hypothesis.testing\$nerr
#> [1] 0
#>
#> \$hypothesis.testing\$fut
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.05 0.11 0.07 0.09 0.07
#>
#>
#> \$sample.size
#>       [,1]  [,2]  [,3]  [,4]  [,5]
#> [1,] 22.66 21.25 21.65 21.61 20.93
#>
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]   23   23   22   23   22
#>
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]    4    4    4    4    4
#>
#>      [,1] [,2] [,3] [,4] [,5]
#> [1,]   35   34   36   32   34
#>
#> \$sample.size\$overall.ave
#>       [,1]
#> [1,] 108.1
#>
#>
#> \$point.estimation
#> \$point.estimation\$PM.ave
#>           [,1]      [,2]      [,3]      [,4]      [,5]
#> [1,] 0.4424834 0.4289148 0.4317818 0.4387614 0.4250887
#>
#> \$point.estimation\$SP.ave
#>           [,1]      [,2]      [,3]     [,4]      [,5]
#> [1,] 0.4391716 0.4222146 0.4279804 0.435188 0.4190648
#>
#> \$point.estimation\$PP.ave
#>           [,1]      [,2]      [,3]    [,4]      [,5]
#> [1,] 0.9711071 0.9174152 0.9445281 0.94365 0.9445365
#>
#> \$point.estimation\$bias
#>              [,1]        [,2]        [,3]        [,4]        [,5]
#> [1,] -0.007516613 -0.02108521 -0.01821818 -0.01123862 -0.02491135
#>
#> \$point.estimation\$mse
#>             [,1]       [,2]       [,3]       [,4]       [,5]
#> [1,] 0.009406759 0.02019997 0.01433251 0.01463391 0.01342345
#>
#>
#> \$trial.duration
#> \$trial.duration\$average
#> [1] 61.73358
#>
#>
#> \$early.stopping
#> \$early.stopping\$interim.stop.prob
#>      [,1] [,2]
#> [1,] 0.03 0.97
#>