repeatedPValue {lrstat}

Repeated p-values for group sequential design

Description

Obtains the repeated p-values for a group sequential design.

Usage

repeatedPValue(
  kMax,
  typeAlphaSpending = "sfOF",
  parameterAlphaSpending = NA,
  maxInformation = 1,
  p,
  information,
  spendingTime = NULL
)

Arguments

kMax

The maximum number of stages.

typeAlphaSpending

The type of alpha spending. One of the following: "OF" for O'Brien-Fleming boundaries, "P" for Pocock boundaries, "WT" for Wang & Tsiatis boundaries, "sfOF" for O'Brien-Fleming type spending function, "sfP" for Pocock type spending function, "sfKD" for Kim & DeMets spending function, "sfHSD" for Hwang, Shi & DeCani spending function, "user" for user defined spending, and "none" for no early efficacy stopping. Defaults to "sfOF".

parameterAlphaSpending

The parameter value for the alpha spending. Corresponds to Delta for "WT", rho for "sfKD", and gamma for "sfHSD".

maxInformation

The target maximum information. Defaults to 1, in which case, information represents informationRates.

p

The raw p-values at look 1 to look k. It can be a matrix with k columns for k <= kMax.

information

The observed information by look. It can be a matrix with k columns.

spendingTime

The error spending time at each analysis, must be increasing and less than or equal to 1. Defaults to NULL, in which case, it is the same as informationRates derived from information and maxInformation. It can be a matrix with k columns.

Value

The repeated p-values at look 1 to look k.

Author(s)

Kaifeng Lu, [e-mail]

Examples

# Example 1: informationRates different from spendingTime
repeatedPValue(kMax = 3, typeAlphaSpending = "sfOF",
               maxInformation = 800,
               p = c(0.2, 0.15, 0.1),
               information = c(529, 700, 800),
               spendingTime = c(0.6271186, 0.8305085, 1))

# Example 2: Maurer & Bretz (2013), current look is not the last look
repeatedPValue(kMax = 3, typeAlphaSpending = "sfOF",
               p = matrix(c(0.0062, 0.017,
                            0.009, 0.13,
                            0.0002, 0.0035,
                            0.002, 0.06),
                          nrow=4, ncol=2),
               information = c(1/3, 2/3))