bmabasket: Bayesian Model Averaging for Basket Trials

Description

An implementation of the Bayesian model averaging method of Psioda and others (2019) <doi:10.1093/biostatistics/kxz014> for basket trials. Contains a user-friendly wrapper for simulating basket trials under conditions and analyzing them with a Bayesian model averaging approach.

Author(s)

Maintainer: Matt Psioda matt_psioda@unc.edu

Authors:

• Ethan Alt ethanalt@live.unc.edu

See Also

Useful links:

• https://github.com/ethan-alt/bmabasket

• Report bugs at https://github.com/ethan-alt/bmabasket/issues

Compute posterior model probabilities

Description

Given data and hyperparameters, computes posterior model probabilities

Usage

bma(pi0, y, n, P = NULL, mu0 = 0.5, phi0 = 1, priorModelProbs = NULL, pmp0 = 1)

Arguments

Value

a list with the following structure:

bmaProbs

model-averaged probabilities that each basket is larger than pi0

bmaMeans

model-averaged posterior mean for each basket

Examples

## Simulate data with 3 baskets
probs <- c(0.5, 0.25, 0.25)
n <- rep(100, length(probs))
y <- rbinom(length(probs), size = n, prob = probs)
bma(0.5, y, n)

Simulate a BMA design

Description

Simulates a BMA design given hyperparameters

Usage

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
)

Arguments

Value

a nested list giving results of the simulation with the following structure:

• hypothesis.testing - hypothesis testing information

  • rr - basket-specific null hypothesis rejection rate

  • fw.fpr - family-wise false positive rate (across all inactive baskets)

  • nerr - average number of false null hypothesis rejections

  • fut - basket-specific probability of futility stopping

• sample.size - trial sample size information

  • basket.ave - basket-specific expected sample size

  • basket.med - basket-specific median sample size

  • basket.min - basket-specific minimum sample size

  • basket.max - basket-specific maximum sample size

  • overall.ave - expected overall sample size

• point.estimation - point estimation information

  • PM.ave - basket-specific average posterior mean

  • SP.ave - basket-specific average sample proportion

  • PP.ave - basket-specific average posterior probability

  • bias - basket-specific bias of the posterior mean

  • mse - basket-specific 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

Examples

## 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
)

Compute number of models

Description

Given a basket size and maximal number of distinct response rates, compute the number of possible models

Usage

numModels(K, P)

Arguments

Value

integer giving number of possible models

Examples

numModels(10, 10)