hbdct (version 1.0.1)

find.n: finding satisfactory sample size for a hierarchical Bayesian Clinical Trial design

Description

generates artificial data from binomial model, samples interested parameters from posterior distributions, simulates the operating characteristics, and chooses a design (sample size) that delivers pre-specified desired operating characteristics.

Usage

find.n(del.cut, k, T, p.c, effect.size, alpha.target = 0.1, 
       power.target = 0.9, tol.b1 = 0.02, tol.b2 = 0.02, tol.a = 0.05, 
	   n.min = 5, n.max = 70, nstep=10, rep = 500, pathcode = path.package("hbdct"), 
	   codefile = "BUGS_Bin_2arm.txt", pathout = getwd(), method = "linear")

Arguments

del.cut

$delta_cut$, efficacy boundary

k

number of tumor types

T

$T_p$, threshold probability

p.c

$pi_C$, a vector of true response rates for k tumor type in control arm

effect.size

$delta_i$, effect size to dectect in treatment group

alpha.target

desired family-wise Type-I error rate, default is 0.1.

power.target

desired level of individual power for each type of tumor, default is 0.9.

tol.b1

tolerance of lower bound of target individual power rate, default is 0.02.

tol.b2

tolerance of upper bound of target individual power rate, default is 0.02.

tol.a

tolerance of upper bound of target family-wise Type\_I error, default is 0.05.

n.min

the minimum sample size needed, default is 5.

n.max

the maximum sample size afforded, default is 70.

nstep

the search precision of "linear" method. Linear method starts searching from nmin, and linearly increases n by nstep each time to find the optimal sample size.

rep

number of repetition of generating posteriors $Pr(delta_i > delta_cut | data) > T_p$, default is 500.

pathcode

path to the BRugs model file. If missing, current working directory will be used to search for BRugs model.

codefile

name of the file containing the BRugs model. Defualt is "BUGS\_Bin\_2arm.txt".

pathout

path for the output files. If missing, current working directory will be used to search for BRugs model.

method

available methods "linear" or "bisect". "linear" method linearly increases the sample size from n.min by nstep each time until it hits the optimal sample size or until it reaches n.max. "bisect" method use the Bisection method to find the optimal sample size. Default is "linear" method.

Value

For each sample size searched ($n_searched$), find.n generates text file "alt5\_0.025\_<$n_searched$>.txt" and "null\_0.025\_<$n_searched$>.txt" to store posteriors ${ Pr(delta_i > 0.025 | data) > 0.85; i=1,…,k }$ at each one of $N_rep$ repetitions under alternatives and null hypothesis. After $N_rep$ repetitions, the Operating Characteristics corresponding to $n_searched$ is output to text file "history\_linear.txt". Once the searching-for-sample-size process is over, find.n outputs a message whether the satisfactory sample size is found or not. Meanwhile, the Operating Characteristics related to the final sample size is print out.

del.cut

efficacy boundary $delta_cut$

T

threshold probability $T_p$

n

final sample size per arm per group

alpha.fw

final family-wise Type-I error rate

power.ind1,…,power.indk

final individual power rates for each type of tumor

Examples

## IMPORTANT: Please install package BRugs
## use Linear method
#find.n(del.cut=0.025, k=3, T=0.85, p.c=c(0.5, 0.4, 0.65), effect.size=0.2,
#        alpha.target=0.1, power.target=0.9, 
#        tol.b1=0.02, tol.b2=0.02, tol.a=0.05,
#        rep=500, pathcode=path.package("hbdct"), codefile="BUGS_Bin_2arm.txt", pathout=getwd())

## use Bisect method
#find.n(del.cut=0.025, k=3, T=0.85, p.c=c(0.5, 0.4, 0.65), effect.size=0.2,
#        alpha.target=0.1, power.target=0.9, 
#        tol.b1=0.02, tol.b2=0.02, tol.a=0.05,
#        rep=500, pathcode=path.package("hbdct"), codefile="BUGS_Bin_2arm.txt", pathout=getwd(),
#        method="bisect")