main.function {exact.n}

Provide sample size solutions for target size and power.

Description

Function gives smallest values for n1 as function of n0 that achieve target size and power.

Usage

main.function(
  alpha,
  delta,
  beta = 0.75,
  p0 = 0.5,
  type = 2,
  plt = FALSE,
  out = (-1),
  b.lim = 5,
  prin = TRUE
)

Arguments

alpha

value of nominal size of test

delta

value of clinically relevant difference

beta

scalar target for power

p0

single value or range of values for baseline probability

type

type of maximisation (see n1.get documentation)

plt

If TRUE, plot n1 solutions versus

out

More solutions output if out > 0 than out < 0 (see details)

b.lim

maximum imbalance of sample sizes

prin

If TRUE, error messages will be printed.

Details

If out > 0 all solutions (including n1=Inf) are returned. If out=0, infinite values are suppressed. If out < 0, only output satisfying the balance criterion are output.

Value

list with elements n0 and n1

Note

The appropriate data file needs to have been downloaded corresponding to the desired value of alpha and delta. This can be done with the fetch.data() function.

Author(s)

Chris J. Lloyd

References

C.J. Lloyd (2022) Exact samples sizes for clinical trials subject to size and power constraints. Preprint. doi:10.13140/RG.2.2.11828.94085

Examples

# We are interested in designs with power at least 0.75 when exact size
# 0.025 and delta=0.20. Therefore, you would need to have downloaded
# LIB.a025.d20 using fetch(0.015,0.20). The example below instead uses
# the toy data that comes with the package. The baseline probability is
# assumed to be between 0.3 and 0.5.
rdata_file = system.file('files', 'LIB.a025.d20.Rdata', package = 'exact.n')
load(rdata_file)
#' main.function(.025,0.20,p0=c(0.3,0.5),beta=0.75,plt=TRUE)
# The value of the function is the minimum value of n1 for a range
# of values of n0. The sample size ratio is limited to 5 by default.