(Conditional) Sample Size of a Design

Source: R/ConditionalSampleSize.R

This score simply evaluates n(d, x1) for a design d and the first-stage outcome x1. The data distribution and prior are only relevant when it is integrated.

ConditionalSampleSize(label = "n(x1)")

ExpectedSampleSize(dist, prior, label = "E[n(x1)]")

ExpectedNumberOfEvents(dist, prior, label = "E[n(x1)]")

# S4 method for ConditionalSampleSize,TwoStageDesign
evaluate(s, design, x1, optimization = FALSE, ...)

Arguments

label

object label (string)

dist

a univariate distribution object

prior

a Prior object

s

Score object

design

object

x1

stage-one test statistic

optimization

logical, if TRUE uses a relaxation to real parameters of the underlying design; used for smooth optimization.

...

further optional arguments

See also

Scores

Examples

design <- TwoStageDesign(50, .0, 2.0, 50, 2.0, order = 5L)
prior  <- PointMassPrior(.4, 1)

css   <- ConditionalSampleSize()
evaluate(css, design, c(0, .5, 3))
#> [1] 100 100  50

ess   <- ExpectedSampleSize(Normal(), prior)

ene <- ExpectedNumberOfEvents(Survival(0.7),PointMassPrior(1.7,1))

# those two are equivalent
evaluate(ess, design)
#> [1] 73.86249
evaluate(expected(css, Normal(), prior), design)
#> [1] 73.86249