gs_spending_combo: Derive spending bound for MaxCombo group sequential boundary

In gsDesign2: Group Sequential Design with Non-Constant Effect

Derive spending bound for MaxCombo group sequential boundary

Description

Derive spending bound for MaxCombo group sequential boundary

Usage

gs_spending_combo(par = NULL, info = NULL)

Arguments

par

A list with the following items: sf (class spending function). total_spend (total spend). param (any parameters needed by the spending function sf()). timing (a vector containing values at which spending function is to be evaluated or NULL if information-based spending is used). max_info (when timing is NULL, this can be input as positive number to be used with info for information fraction at each analysis).

info

Statistical information at all analyses, at least up to analysis k.

Value

A vector of the alpha spending per analysis.

Examples

# alpha-spending
par <- list(sf = gsDesign::sfLDOF, total_spend = 0.025)
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfLDPocock, total_spend = 0.025)
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfHSD, total_spend = 0.025, param = -40)
gs_spending_combo(par, info = 1:3 / 3)

# Kim-DeMets (power) Spending Function
par <- list(sf = gsDesign::sfPower, total_spend = 0.025, param = 1.5)
gs_spending_combo(par, info = 1:3 / 3)

# Exponential Spending Function
par <- list(sf = gsDesign::sfExponential, total_spend = 0.025, param = 1)
gs_spending_combo(par, info = 1:3 / 3)

# Two-parameter Spending Function Families
par <- list(sf = gsDesign::sfLogistic, total_spend = 0.025, param = c(.1, .4, .01, .1))
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfBetaDist, total_spend = 0.025, param = c(.1, .4, .01, .1))
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfCauchy, total_spend = 0.025, param = c(.1, .4, .01, .1))
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfExtremeValue, total_spend = 0.025, param = c(.1, .4, .01, .1))
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfExtremeValue2, total_spend = 0.025, param = c(.1, .4, .01, .1))
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfNormal, total_spend = 0.025, param = c(.1, .4, .01, .1))
gs_spending_combo(par, info = 1:3 / 3)

# t-distribution Spending Function
par <- list(sf = gsDesign::sfTDist, total_spend = 0.025, param = c(-1, 1.5, 4))
gs_spending_combo(par, info = 1:3 / 3)

# Piecewise Linear and Step Function Spending Functions
par <- list(sf = gsDesign::sfLinear, total_spend = 0.025, param = c(.2, .4, .05, .2))
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfStep, total_spend = 0.025, param = c(1 / 3, 2 / 3, .1, .1))
gs_spending_combo(par, info = 1:3 / 3)

# Pointwise Spending Function
par <- list(sf = gsDesign::sfPoints, total_spend = 0.025, param = c(.25, .25))
gs_spending_combo(par, info = 1:3 / 3)

# Truncated, trimmed and gapped spending functions
par <- list(sf = gsDesign::sfTruncated, total_spend = 0.025,
  param = list(trange = c(.2, .8), sf = gsDesign::sfHSD, param = 1))
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfTrimmed, total_spend = 0.025,
  param = list(trange = c(.2, .8), sf = gsDesign::sfHSD, param = 1))
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfGapped, total_spend = 0.025,
  param = list(trange = c(.2, .8), sf = gsDesign::sfHSD, param = 1))
gs_spending_combo(par, info = 1:3 / 3)

# Xi and Gallo conditional error spending functions
par <- list(sf = gsDesign::sfXG1, total_spend = 0.025, param = 0.5)
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfXG2, total_spend = 0.025, param = 0.14)
gs_spending_combo(par, info = 1:3 / 3)

par <- list(sf = gsDesign::sfXG3, total_spend = 0.025, param = 0.013)
gs_spending_combo(par, info = 1:3 / 3)

# beta-spending
par <- list(sf = gsDesign::sfLDOF, total_spend = 0.2)
gs_spending_combo(par, info = 1:3 / 3)

gsDesign2 documentation built on April 3, 2025, 9:39 p.m.