Title: Clinical Trial Calculation Based on BCHM Design
Version: 1.00
Author: Nan Chen and J. Jack Lee
Description: Users can estimate the treatment effect for multiple subgroups basket trials based on the Bayesian Cluster Hierarchical Model (BCHM). In this model, a Bayesian non-parametric method is applied to dynamically calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the Bayesian non-parametric clustering and the similarities between subgroups. To use this package, 'JAGS' software and 'rjags' package are required, and users need to pre-install them.
Depends: R (≥ 3.5), rjags
License: LGPL-2
Maintainer: J. Jack Lee <jjlee@mdanderson.org>
Imports: stats, cluster, coda, knitr, crayon, plyr
Suggests: testthat
LazyData: true
Encoding: UTF-8
SystemRequirements: JAGS
RoxygenNote: 7.0.2
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-06-04 18:44:23 UTC; nchen2
Repository: CRAN
Date/Publication: 2020-06-05 22:00:13 UTC

Perform the analysis based on the BCHM design.

Description

The BCHM computation is based on the Bayesian Cluster Hierarchical Model (BCHM) to perform the non-parametric Bayesian clustering and posterior probability calculation with the Bayesian hierarchical model for binary response data in several subgroups. Due to the potential heterogeneity among subgroups, the exchangeability assumption across all subgroups may not hold. A Bayesian non-parametric method is applied to calculate the number of clusters by conducting the multiple cluster classification based on subgroup outcomes. Due to the MCMC sampling, the clustering result is dynamic. A cluster matrix (Similarity Matrix) is constructed to depict the strength of association between any two subgroups to be classified into the same cluster. The Bayesian hierarchical model is used to compute the posterior probability of treatment effect with the borrowing strength determined by the similarity matrix values calculated from the Bayesian non-parametric clustering.

Usage

BCHM(
  nDat,
  xDat,
  mu = 0.2,
  sigma02 = 10,
  sigmaD2 = 0.001,
  alpha = 1e-60,
  d0 = 0.05,
  alpha1 = 50,
  beta1 = 10,
  tau2 = 0.1,
  phi1 = 0.1,
  deltaT = 0.05,
  thetaT = 0.6,
  burnIn = 10000,
  MCIter = 20000,
  MCNum = 20000,
  seed = 1000
)

Arguments

nDat

Number of patients in each subgroup.

xDat

Number of responses in each subgroup.

mu

Hyperprior mean for the cluster.

sigma02

Hyperprior variance for the cluster.

sigmaD2

Variance of subgroup response rate.

alpha

Alpha value of the Dirichlet Process determining number of clusters.

d0

Minimum value for the similarity matrix.

alpha1

Prior for borrowing strength gamma(alpha1, beta1) in the hierarchical model.

beta1

Prior for borrowing strength gamma(alpha1, beta1) in the hierarchical model.

tau2

Hyperprior precision parameter of subgroup means in the hierarchical model

phi1

The response rate of the standard treatment.

deltaT

The expected improvement in the response rate over the standard treatment.

thetaT

Threshold value for the inference claiming efficacy.

burnIn

Number of burn_in in MCMC.

MCIter

Number of MCMC iterations.

MCNum

Number of MCMC iterations in the hierarchical model.

seed

Random seed.

Value

The return is a list including three elements: Samples, SMatrix, and Result.

The return list element Samples is the sampled posterior response rates of all subgroups.

The return list element SMatrix is the similarity matrix of all subgroups.

The return list element Result is the calculation results of all subgroups. It has seven columns: the number of responses of each subgroups, the number of patients in each subgroups, the observe response rates of each subgroups, the cluster index of each subgroups, the posterior mean response rates of each subgroups, the probability of Pr(P>Phi1+deltaT) of each subgroups, and the final decision (0: Not rejected the null, 1 Rejected the null). Note: Because a MCMC computation is applied in the clustering calculation, there are many possible clustering configurations. The cluster index in column 4 is the most possible clustering configuration.

See Also

BCHMplot_cluster Plot the clustering results of subgroups.

BCHMplot_post_value Plot the posterior response of subgroups.

BCHMplot_post_dist Plot the posterior distributions of subgroups.

Examples

nDat = c(25, 25, 25, 25) # total number of patients
xDat = c(2, 3, 8, 6)  # number of responses
alpha <- 1e-20 
d0 <- 0.0 
alpha1 = 50   
beta1 = 10  
tau2 <- 0.1  
phi1 <- 0.1  
deltaT <- 0.2  
thetaT <- 0.60   

res <- BCHM(nDat = nDat,
            xDat = xDat,
            alpha = alpha,
            d0 = d0,             
            alpha1 = alpha1, 
            beta1 = beta1,
            tau2 = tau2,
            phi1 = phi1, 
            deltaT = deltaT,
            thetaT = thetaT,
            burnIn = 100,
            MCIter = 200,
            MCNum = 1000,
            seed = 1000
)
print(res$SMatrix)
print(res$Result)
col <- res$Result[,4]

BCHMplot_cluster(res, col, pch=16)
BCHMplot_post_value(res, col, HPD = 0.8)
BCHMplot_post_dist(res, col, lty=1:length(nDat), lwd =3, xlim=c(0, 0.8))

Plot the clustering results of subgroups.

Description

plot the observed response rate versus subgroup ID with clusters coded by the color of dots.

Usage

BCHMplot_cluster(
  res,
  col = res$Result[, 4],
  pch = 16,
  xlim = c(0, dim(res$Result)[1] + 2),
  ylim = c(0, 1),
  cex = 2,
  ...
)

Arguments

res

BCHM calculation results.

col

Color vector

pch

pch vector

xlim

X-axis range

ylim

Y-axis range

cex

size of points

...

other options

Value

None

See Also

BCHM Perform the analysis based on the BCHM design.

BCHMplot_post_value Plot the posterior response of subgroups.

BCHMplot_post_dist Plot the posterior distributions of subgroups.

Examples

nDat = c(25, 25, 25, 25) # total number of patients
xDat = c(2, 3, 8, 6)  # number of responses
alpha <- 1e-20 
d0 <- 0.0 
alpha1 = 50   
beta1 = 10  
tau2 <- 0.1  
phi1 <- 0.1  
deltaT <- 0.2  
thetaT <- 0.60   

res <- BCHM(nDat = nDat,
            xDat = xDat,
            alpha = alpha,
            d0 = d0,             
            alpha1 = alpha1, 
            beta1 = beta1,
            tau2 = tau2,
            phi1 = phi1, 
            deltaT = deltaT,
            thetaT = thetaT,
            burnIn = 100,
            MCIter = 200,
            MCNum = 1000,
            seed = 1000
)
print(res$SMatrix)
print(res$Result)
col <- res$Result[,4]

BCHMplot_cluster(res, col, pch=16)

Plot the posterior distributions of subgroups.

Description

plot the posterior distribution by subgroup ID

Usage

BCHMplot_post_dist(
  res,
  col = res$Result[, 4],
  lty = 1:dim(res$Result)[1],
  lwd = 2,
  xlim = c(0, 1),
  ylim = c(0, 20),
  ...
)

Arguments

res

BCHM calculation results.

col

Color vector

lty

line types

lwd

line width

xlim

X-axis range

ylim

Y-axis range

...

other options

Value

None

See Also

BCHM Perform the analysis based on the BCHM design.

BCHMplot_cluster Plot the clustering results of subgroups.

BCHMplot_post_value Plot the posterior response of subgroups.

Examples

nDat = c(25, 25, 25, 25) # total number of patients
xDat = c(2, 3, 8, 6)  # number of responses
alpha <- 1e-20 
d0 <- 0.0 
alpha1 = 50   
beta1 = 10  
tau2 <- 0.1  
phi1 <- 0.1  
deltaT <- 0.2  
thetaT <- 0.60   

res <- BCHM(nDat = nDat,
            xDat = xDat,
            alpha = alpha,
            d0 = d0,             
            alpha1 = alpha1, 
            beta1 = beta1,
            tau2 = tau2,
            phi1 = phi1, 
            deltaT = deltaT,
            thetaT = thetaT,
            burnIn = 100,
            MCIter = 200,
            MCNum = 1000,
            seed = 1000
)
print(res$SMatrix)
print(res$Result)
col <- res$Result[,4]

BCHMplot_post_dist(res, col, lty=1:length(nDat), lwd =3, xlim=c(0, 0.8))

Plot the posterior response of subgroups.

Description

plot the posterior response rate with its highest probability density (HPD) interval by subgroup ID

Usage

BCHMplot_post_value(
  res,
  col = res$Result[, 4],
  pch = c(19, 4),
  cex = 2,
  HPD = 0.95,
  xlim = c(0, dim(res$Result)[1] + 2),
  ylim = c(0, 1),
  ObsMean = FALSE,
  ...
)

Arguments

res

BCHM calculation results.

col

Color vector

pch

pch vector pch[1] Posterior mean pch[2] Observed mean

cex

size of points

HPD

Highest Posterior Density level for drawing (NA: No HPD drawing )

xlim

X-axis range

ylim

Y-axis range

ObsMean

Draw the observed mean of subgroups if this parameter is TRUE

...

other options

Value

None

See Also

BCHM Perform the analysis based on the BCHM design.

BCHMplot_cluster Plot the clustering results of subgroups.

BCHMplot_post_dist Plot the posterior distributions of subgroups.

Examples

nDat = c(25, 25, 25, 25) # total number of patients
xDat = c(2, 3, 8, 6)  # number of responses
alpha <- 1e-20 
d0 <- 0.0 
alpha1 = 50   
beta1 = 10  
tau2 <- 0.1  
phi1 <- 0.1  
deltaT <- 0.2  
thetaT <- 0.60   

res <- BCHM(nDat = nDat,
            xDat = xDat,
            alpha = alpha,
            d0 = d0,             
            alpha1 = alpha1, 
            beta1 = beta1,
            tau2 = tau2,
            phi1 = phi1, 
            deltaT = deltaT,
            thetaT = thetaT,
            burnIn = 100,
            MCIter = 200,
            MCNum = 1000,
            seed = 1000
)
print(res$SMatrix)
print(res$Result)
col <- res$Result[,4]

BCHMplot_post_value(res, col, HPD = 0.8)