View source: R/gs_update_ahr.R
gs_update_ahr | R Documentation |
Group sequential design using average hazard ratio under non-proportional hazards
gs_update_ahr(
x = NULL,
alpha = NULL,
ustime = NULL,
lstime = NULL,
event_tbl = NULL
)
x |
A design created by either |
alpha |
Type I error for the updated design. |
ustime |
Default is NULL in which case upper bound spending time is determined by timing. Otherwise, this should be a vector of length k (total number of analyses) with the spending time at each analysis. |
lstime |
Default is NULL in which case lower bound spending time is determined by timing. Otherwise, this should be a vector of length k (total number of analyses) with the spending time at each analysis. |
event_tbl |
A data frame with two columns: (1) analysis and (2) event,
which represents the events observed at each analysis per piecewise interval.
This can be defined via the |
A list with input parameters, enrollment rate, failure rate, analysis, and bound.
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
# ------------------------------------------------- #
# 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),
test_upper = TRUE,
lower = gs_spending_bound,
lpar = list(sf = sfLDOF, total_spend = beta),
test_lower = c(TRUE, FALSE),
binding = FALSE) %>% to_integer()
planned_event_ia <- x$analysis$event[1]
planned_event_fa <- x$analysis$event[2]
# Updated design with 190 events observed at IA,
# where 50 events observed during the delayed effect.
# IA spending = observed events / final planned events, the remaining alpha will be allocated to FA.
gs_update_ahr(
x = x,
ustime = c(190 / planned_event_fa, 1),
lstime = c(190 / planned_event_fa, 1),
event_tbl = data.frame(analysis = c(1, 1),
event = c(50, 140)))
# Updated design with 190 events observed at IA, and 300 events observed at FA,
# where 50 events observed during the delayed effect.
# IA spending = observed events / final planned events, the remaining alpha will be allocated to FA.
gs_update_ahr(
x = x,
ustime = c(190 / planned_event_fa, 1),
lstime = c(190 / planned_event_fa, 1),
event_tbl = data.frame(analysis = c(1, 1, 2, 2),
event = c(50, 140, 50, 250)))
# Updated design with 190 events observed at IA, and 300 events observed at FA,
# where 50 events observed during the delayed effect.
# IA spending = minimal of planned and actual information fraction spending
gs_update_ahr(
x = x,
ustime = c(min(190, planned_event_ia) / planned_event_fa, 1),
lstime = c(min(190, planned_event_ia) / planned_event_fa, 1),
event_tbl = data.frame(analysis = c(1, 1, 2, 2),
event = c(50, 140, 50, 250)))
# Alpha is updated to 0.05
gs_update_ahr(x = x, alpha = 0.05)
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