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Build the group-sequential design for a GSD_model

make_gsd_design.Rd

Builds the rpact group-sequential design used by calc_dte_assurance_adaptive, apply_GSD_to_trial and single_paired_rep. GSD_model$alpha_spending is the user-specified cumulative alpha spent at each efficacy look GSD_model$alpha_IF (rpact's typeOfDesign = "asUser"). For "PP" and "MatchedZ" futility, the futility look (futility_IF) is added to the information-rate grid with zero alpha spent, so its critical value is Inf and the efficacy critical values are exactly those of the efficacy-only design. For "Beta" futility, GSD_model$beta_spending is the cumulative beta spent at each futility_IF (typeBetaSpending = "bsUser"). Information fractions are rounded to 6 dp before they are compared.

Usage

make_gsd_design(GSD_model)

make_rpact_design_from_GSD_model(GSD_model)

Arguments

GSD_model

A named list with alpha_IF, alpha_spending, futility_type ("none", "Beta", "PP" or "MatchedZ") and, for futility designs, futility_IF (and beta_spending for "Beta"). See calc_dte_assurance_adaptive.

Value

A list with design (the rpact design object; design$informationRates and design$criticalValues give the looks and their Z critical values), IF_all, alpha_spending_full and beta_spending_full.

Details

make_rpact_design_from_GSD_model() is the previous name, kept as an alias for one release.

Examples

# Efficacy look at 75% information and final analysis, with a PP futility
# look at 50% information (zero alpha spent there)
gsd <- make_gsd_design(list(alpha_IF = c(0.75, 1),
                            alpha_spending = c(0.0125, 0.025),
                            futility_type = "PP", futility_IF = 0.5))
gsd$design$informationRates
#> [1] 0.50 0.75 1.00
gsd$design$criticalValues
#> [1]      Inf 2.241403 2.046965