gs_update_ahr {gsDesign2}

Group sequential design using average hazard ratio under non-proportional hazards

Description

Group sequential design using average hazard ratio under non-proportional hazards

Usage

gs_update_ahr(
  x = NULL,
  alpha = NULL,
  ia_alpha_spending = c("actual_info_frac", "min_of_planned_and_actual_info_frac"),
  fa_alpha_spending = c("full_alpha", "info_frac"),
  observed_data = NULL
)

Arguments

x

A original design created by either gs_design_ahr or gs_power_ahr.

alpha

Alpha for the updated design.

ia_alpha_spending

Alpha spending strategy for interim analyses, either "actual_info_frac" (default), or "min_of_planned_and_actual_info_frac".

fa_alpha_spending

Alpha spending strategy for final analysis, either "info_frac" or "full_alpha" (default).

observed_data

a list of observed datasets by analyses.

Value

A list with input parameters, enrollment rate, analysis, and bound.

Examples

library(gsDesign)
library(gsDesign2)
library(dplyr)

alpha <- 0.025
beta <- 0.1
ratio <- 1

# Enrollment
enroll_rate <- define_enroll_rate(
  duration = c(2, 2, 10),
  rate = (1:3) / 3)

# Failure and dropout
fail_rate <- define_fail_rate(
  duration = c(3, Inf), fail_rate = log(2) / 9,
  hr = c(1, 0.6), dropout_rate = .0001)

# IA and FA analysis time
analysis_time <- c(20, 36)

# Randomization ratio
ratio <- 1

# Example A: one-sided design (efficacy only) ----

# Original design
upper <- gs_spending_bound
upar <- list(sf = sfLDOF, total_spend = alpha)
x <- gs_design_ahr(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  alpha = alpha, beta = beta, ratio = ratio,
  info_scale = "h0_info",
  info_frac = NULL,
  analysis_time = c(20, 36),
  upper = gs_spending_bound, upar = upar,
  lower = gs_b, lpar = rep(-Inf, 2),
  test_upper = TRUE, test_lower = FALSE) |> to_integer()

# Observed dataset at IA and FA
set.seed(123)

observed_data <- simtrial::sim_pw_surv(
  n = x$analysis$n[x$analysis$analysis == 2],
  stratum = data.frame(stratum = "All", p = 1),
  block = c(rep("control", 2), rep("experimental", 2)),
  enroll_rate = x$enroll_rate,
  fail_rate = (fail_rate |> simtrial::to_sim_pw_surv())$fail_rate,
  dropout_rate = (fail_rate |> simtrial::to_sim_pw_surv())$dropout_rate)

observed_data_ia <- observed_data |> simtrial::cut_data_by_date(x$analysis$time[1])
observed_data_fa <- observed_data |> simtrial::cut_data_by_date(x$analysis$time[2])

# Example A1 ----
# IA spending = observed events / final planned events
gs_update_ahr(
  x = x,
  ia_alpha_spending = "actual_info_frac",
  fa_alpha_spending = "full_alpha",
  observed_data = list(observed_data_ia, observed_data_fa))

# Example A2 ----
# IA, FA spending = observed events / final planned events
gs_update_ahr(
  x = x,
  ia_alpha_spending = "actual_info_frac",
  fa_alpha_spending = "info_frac",
  observed_data = list(observed_data_ia, observed_data_fa))

# Example A3 ----
# IA spending = min(observed events, planned events) / final planned events
gs_update_ahr(
  x = x,
  ia_alpha_spending = "min_of_planned_and_actual_info_frac",
  fa_alpha_spending = "full_alpha",
  observed_data = list(observed_data_ia, observed_data_fa))

# Example A4 ----
# IA spending = min(observed events, planned events) / final planned events
gs_update_ahr(
  x = x,
  ia_alpha_spending = "min_of_planned_and_actual_info_frac",
  fa_alpha_spending = "info_frac",
  observed_data = list(observed_data_ia, observed_data_fa))

# Example A5 ----
# IA spending = min(observed events, planned events) / final planned events
# alpha is upadted to 0.05
gs_update_ahr(
  x = x,
  alpha = 0.05,
  ia_alpha_spending = "min_of_planned_and_actual_info_frac",
  fa_alpha_spending = "info_frac",
  observed_data = list(observed_data_ia, observed_data_fa))

# Example B: Two-sided asymmetric design,
# beta-spending with non-binding lower bound ----

# Original design
x <- gs_design_ahr(
  enroll_rate = enroll_rate, fail_rate = fail_rate,
  alpha = alpha, beta = beta, ratio = ratio,
  info_scale = "h0_info",
  info_frac = NULL, analysis_time = c(20, 36),
  upper = gs_spending_bound,
  upar = list(sf = sfLDOF, total_spend = alpha, param = NULL),
  test_upper = TRUE,
  lower = gs_spending_bound,
  lpar = list(sf = sfLDOF, total_spend = beta, param = NULL),
  test_lower = c(TRUE, FALSE),
  binding = FALSE) |> to_integer()

# Example B1 ----
# IA spending = observed events / final planned events
# the remaining alpha will be allocated to FA.
gs_update_ahr(
  x = x,
  ia_alpha_spending = "actual_info_frac",
  fa_alpha_spending = "full_alpha",
  observed_data = list(observed_data_ia, observed_data_fa))

# Example B2 ----
# IA, FA spending = observed events / final planned events
gs_update_ahr(
  x = x,
  ia_alpha_spending = "actual_info_frac",
  fa_alpha_spending = "info_frac",
  observed_data = list(observed_data_ia, observed_data_fa))

# Example B3 ----
# IA spending = min(observed events, planned events) / final planned events
gs_update_ahr(
  x = x,
  ia_alpha_spending = "min_of_planned_and_actual_info_frac",
  fa_alpha_spending = "full_alpha",
  observed_data = list(observed_data_ia, observed_data_fa))

# Example B4 ----
# IA spending = min(observed events, planned events) / final planned events
gs_update_ahr(
  x = x,
  ia_alpha_spending = "min_of_planned_and_actual_info_frac",
  fa_alpha_spending = "info_frac",
  observed_data = list(observed_data_ia, observed_data_fa))

# Example A5 ----
# IA spending = min(observed events, planned events) / final planned events
# alpha is upadted to 0.05
gs_update_ahr(
  x = x,
  alpha = 0.05,
  ia_alpha_spending = "min_of_planned_and_actual_info_frac",
  fa_alpha_spending = "info_frac",
  observed_data = list(observed_data_ia, observed_data_fa))