Description Usage Arguments Details Value See Also Examples
A Bayesian average error based approach to sample size determination. These functions compute the required sample size for various designs common to clinical trials. A specified Total Error rate is maintain and more emphasis can be placed on controlling Average TypeI Error or TypeII Error.
1 2 3 
alpha 
Scalar. The bound to maintain on the Total Error rate. Must be a decimal between 0 and 1.  
w 
Scalar. The weight to be given to Average TypeI Error. Larger values of w control TypeI error rates more. Must be a decimal between 0 and 1.  
logm 
Function. Computes the log marginal (prior predictive density) under H0 and H1. This function should return a list that contains two components:
The first parameter of this function should be the observed data. See details for necessary form. The second parameter should be the sample size.  
minn, maxn 
Scalar. The minimum and maximum sample size to consider.  
all 
Boolean. If  
two.sample 
Boolean. If  
... 
Additional parameters to be passed to 
Sample size calculations are dependent upon the knowledge of the marginal
density under each hypothesis. The function logm
should provide these
densities.
For a onesample binomial experiment, the first argument of logm
should
be a vector in which each entry represents a different number of successes
out of n independent binary trials. For a twosample binomial experiment, the
first argument of logm
should be a matrix, in which each row represents
the number of successes for each of the samples out of n indpendent trials.
For an example see binom1.1sided
.
For a onesample normal experiment with known variance, the first argument
shoul be a vector of different sample means. For an example see
norm1KV.1sided
.
In addition, there are a few functions specific to a given situation (or suite); these are
ssd.norm1KV.2sided(alpha, w, sigma, theta0, prob, mu, tau, minn = 2, maxn = 1000, all = FALSE)
ssd.norm2KV.2sided(alpha, w, sigma, prob, mu0, tau0, mu1, tau1, mu2, tau2, m = 2500, minn = 2, maxn = 1000, all = FALSE)
ssd.norm1UV.2sided(alpha, w, theta0, prob, mu, scale, shape, rate, m = 2500, minn = 3, maxn = 1000, all = FALSE)
The only parameter unique to these functions is
m
: The number of Monte Carlo replicates to use in computing
the average errors.
The logm
function is not required as the function is specific to that
suite. Instead, various parameters unique to that suite are required. See
the corresponding suite for details.
An object of class "BAEssd" which is a list containing the following elements:
call 
The call to the function. 
history 
Dataframe. Contains one row for each sample size considered during the function evaluation. Each row records the sample size attempted, the average typeI and typeII errors, the total weighted error, and the total error. 
n 
Scalar. The chosen sample size with attributes related to the function call and total error. 
summary.BAEssd
,plot.BAEssd
,
binom1.1sided
,binom1.2sided
,
binom2.1sided
,binom2.2sided
,
norm1KV.1sided
,norm1KV.2sided
,
norm2KV.2sided
,norm1UV.2sided
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34  ############################################################
# Computing a sample size for a onesample binomial
# experiment with a twosided alternative.
# load suite of functions
f1 < binom1.2sided(p0=0.5,prob=0.5,a=1,b=1)
# calculate sample size for total error bound of 0.25 and weight 0.5
attach(f1)
ss1 < ssd.binom(alpha=0.25,w=0.5,logm=logm,two.sample=FALSE)
detach(f1)
# see results
ss1
# examine structure
str(ss1)
############################################################
# Computing a sample size for a onesample normal
# experiment with a twosided alternative using the
# functions internal to the suite.
# load suite of functions
f2 < norm1KV.2sided(sigma=5,theta0=0,prob=0.5,mu=2,tau=1)
# calculate sample size for total error bound of 0.25 and weight 0.5
attach(f2)
ss2 < ssd.norm1KV.2sided(alpha=0.25,w=0.5)
detach(f2)
# see results
ss2

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