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)
#> [1] 0.0001035057 0.0060483891 0.0250000000

par <- list(sf = gsDesign::sfLDPocock, total_spend = 0.025)
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 0.01132081 0.01908456 0.02500000

par <- list(sf = gsDesign::sfHSD, total_spend = 0.025, param = -40)
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 6.557724e-14 4.048992e-08 2.500000e-02

# 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)
#> [1] 0.004811252 0.013608276 0.025000000

# Exponential Spending Function
par <- list(sf = gsDesign::sfExponential, total_spend = 0.025, param = 1)
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 0.000015625 0.003952847 0.025000000

# 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)
#> [1] 0.001757277 0.008146545 0.025000000

par <- list(sf = gsDesign::sfBetaDist, total_spend = 0.025, param = c(.1, .4, .01, .1))
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 0.001818609 0.006568999 0.025000000

par <- list(sf = gsDesign::sfCauchy, total_spend = 0.025, param = c(.1, .4, .01, .1))
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 0.001378849 0.023755732 0.025000000

par <- list(sf = gsDesign::sfExtremeValue, total_spend = 0.025, param = c(.1, .4, .01, .1))
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 0.001785159 0.007184159 0.025000000

par <- list(sf = gsDesign::sfExtremeValue2, total_spend = 0.025, param = c(.1, .4, .01, .1))
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 0.001799588 0.007015878 0.025000000

par <- list(sf = gsDesign::sfNormal, total_spend = 0.025, param = c(.1, .4, .01, .1))
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 0.001797471 0.006969761 0.025000000

# 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)
#> [1] 0.002063494 0.009705759 0.025000000

# 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)
#> [1] 0.00375000 0.01388889 0.02500000

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)
#> [1] 0.0025 0.0025 0.0250

# Pointwise Spending Function
par <- list(sf = gsDesign::sfPoints, total_spend = 0.025, param = c(.25, .25))
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 0.00625 0.00625 0.02500

# 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)
#> [1] 0.00788072 0.02137939 0.02500000

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)
#> [1] 0.01121102 0.01924407 0.02500000

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)
#> [1] 0.007169093 0.007169093 0.025000000

# 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)
#> [1] 0.0001035057 0.0060483891 0.0250000000

par <- list(sf = gsDesign::sfXG2, total_spend = 0.025, param = 0.14)
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 0.008419231 0.021216583 0.025000000

par <- list(sf = gsDesign::sfXG3, total_spend = 0.025, param = 0.013)
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 0.02428922 0.02477989 0.02500000

# beta-spending
par <- list(sf = gsDesign::sfLDOF, total_spend = 0.2)
gs_spending_combo(par, info = 1:3 / 3)
#> [1] 0.02643829 0.11651432 0.20000000