bma_design {bmabasket}  R Documentation 
Simulates a BMA design given hyperparameters
bma_design( nSims, nBaskets, maxDistinct = nBaskets, eRates, rRates, meanTime, sdTime, ppEffCrit, ppFutCrit, futOnly = FALSE, rRatesNull, rRatesAlt, minSSFut, minSSEff, minSSEnr, maxSSEnr, targSSPer, I0, mu0 = 0.5, phi0 = 1, priorModelProbs = NULL, pmp0 = 1 )
nSims 
number of simulation studies to be performed 
nBaskets 
number of baskets 
maxDistinct 
integer between 1 and 
eRates 
scalar or vector of Poisson process rates for each basket 
rRates 
scalar or vector of true response rates for each basket 
meanTime 
mean parameter for time to outcome ascertainment 
sdTime 
standard deviation parameter for time to outcome ascertainment 
ppEffCrit 
scalar or vector giving basketspecific posterior probability threshold for activity (i.e., efficacy). 
ppFutCrit 
scalar or vector giving basketspecific posterior probability threshold for futility 
futOnly 

rRatesNull 
scalar or vector of basketspecific null hypothesis values (for efficacy determination) 
rRatesAlt 
scalar or vector of basketspecific hypothesized alternative values (for futility determination) 
minSSFut 
minimum number of subjects in basket to assess futility 
minSSEff 
minimum number of subjects in basket to assess activity 
minSSEnr 
matrix giving minimum number of new subjects per basket before next analysis (each row is an interim analysis, each column is a basket) 
maxSSEnr 
matrix giving maximum number of new subjects per basket before next analysis (each row is an interim analysis, each column is a basket) 
targSSPer 
scalar or vector giving target sample size increment for each basket 
I0 
maximum number of analyses 
mu0 
prior mean for the response probabilities 
phi0 
prior dispersion response probabilities 
priorModelProbs 
vector giving prior probabilities for models. Default is prior of each model is proportional 
pmp0 
scalar giving power for 
a nested list giving results of the simulation with the following structure:
hypothesis.testing  hypothesis testing information
rr  basketspecific null hypothesis rejection rate
fw.fpr  familywise false positive rate (across all inactive baskets)
nerr  average number of false null hypothesis rejections
fut  basketspecific probability of futility stopping
sample.size  trial sample size information
basket.ave  basketspecific expected sample size
basket.med  basketspecific median sample size
basket.min  basketspecific minimum sample size
basket.max  basketspecific maximum sample size
overall.ave  expected overall sample size
point.estimation  point estimation information
PM.ave  basketspecific average posterior mean
SP.ave  basketspecific average sample proportion
PP.ave  basketspecific average posterior probability
bias  basketspecific bias of the posterior mean
mse  basketspecific MSE of the posterior mean
early.stopping  early stopping information
interim.stop.prob  probability of trial stoppage by interim
baskets.continuing.ave  average number of baskets continuing past interim
## SIMULATE DATA AND SET SIMULATION PARAMS nSims < 100 ## would be much more in practice 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 )