BAEssd-package {BAEssd} R Documentation

## Bayesian Average Error approach to Sample Size Determination

### Description

Employes a Bayesian average error based approach to sample size determination. Several functions are included for sample size calculation for common designs in clinical trials including one- and two-sample binary and normal responses. These functions are grouped in "suites" that pertain to each type of example.

### Details

 Package: BAEssd Type: Package Version: 1.0.1 Date: 2010-06-18 Suggests: mvtnorm License: GPL-2 LazyLoad: yes

Before any calculations can be made, first identify the example of interest and the corresponding suite of functions:

 `binom1.1sided` One sample, binary response, one-sided hypothesis. `binom1.2sided` One sample, binary response, two-sided hypothesis. `binom2.1sided` Two indepednent samples, binary response, one-sided hypothesis. `binom2.2sided` Two independent samples, binary response, two-sided hypothesis. `norm1KV.1sided` One sample, normal response, known variance, one-sided hypothesis. `norm1KV.2sided` One sample, normal response, known variance, two-sided hypothesis. `norm1UV.2sided` One sample, normal response, unknown variance, two-sided hypothesis. `norm2KV.2sided` Two independent samples, normal response, known variance, two-sided hypothesis.

After selecting the suite of functions of interest, the suite must be generated with appropriate parameters. Then, the corresponding `ssd` function can be used to calculate the sample size. The two primary constraints for choosing a sample size are the bound on the Total Error to maintain and the weight given to controlling the Average Type-I Error (as opposed to the Average Type-II Error).

### Examples

```############################################################
# Calculate the sample size required for a one-sample
# normal experiment with known variance (sigma2=25) with
# the hypothesis of interest being
#     H0: theta==0   vs.   H1: theta!=0
#
# where theta is the mean of the normal distribution. For
# details on the prior used, see documentation for
# norm1KV.2sided().

# generate suite of functions
f1 <- norm1KV.2sided(sigma=5,theta0=0,prob=0.5,mu=2,tau=1)

# attach suite
attach(f1)

# calculate sample size for TE bound of 0.25 and weight 0.5
ssd.norm1KV.2sided(alpha=0.25,w=0.5)

# detach suite
detach(f1)

############################################################
# Calculate the sample size required for a two-sample
# experiment with a binary response in which the hypothesis
# of interest is
#     H0: p1==p2   vs.   H1: p1!=p2
#
# where p1 is the response rate for group 1 and p2 is the
# response rate for group 2, independent samples. For
# details on the prior used, see documentation for
# binom2.2sided().

# generate suite of functions
f2 <- binom2.2sided(prob=0.5,a0=1,b0=1,a1=1,b1=1,a2=1,b2=1)

# attach suite
attach(f2)

# calculate sample size for TE bound of 0.25 and weight 0.5
#   - here the log marginal distribution (logm) is part of the suite.
ssd.binom(alpha=0.25,w=0.5,logm=logm,two.sample=TRUE)

# detach suite
detach(f2)
```

[Package BAEssd version 1.0.1 Index]