gsDesign2 (version 1.1.2)

gs_design_wlr: Group sequential design using weighted log-rank test under non-proportional hazards

Description

Group sequential design using weighted log-rank test under non-proportional hazards

Usage

gs_design_wlr(
  enroll_rate = define_enroll_rate(duration = c(2, 2, 10), rate = c(3, 6, 9)),
  fail_rate = tibble(stratum = "All", duration = c(3, 100), fail_rate = log(2)/c(9, 18),
    hr = c(0.9, 0.6), dropout_rate = rep(0.001, 2)),
  weight = wlr_weight_fh,
  approx = "asymptotic",
  alpha = 0.025,
  beta = 0.1,
  ratio = 1,
  info_frac = NULL,
  info_scale = c("h0_h1_info", "h0_info", "h1_info"),
  analysis_time = 36,
  binding = FALSE,
  upper = gs_b,
  upar = gsDesign(k = 3, test.type = 1, n.I = c(0.25, 0.75, 1), sfu = sfLDOF, sfupar =
    NULL)$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(0.1), -Inf, -Inf),
  test_upper = TRUE,
  test_lower = TRUE,
  h1_spending = TRUE,
  r = 18,
  tol = 1e-06,
  interval = c(0.01, 1000)
)

Value

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

Arguments

enroll_rate

Enrollment rates.

fail_rate

Failure and dropout rates.

weight

Weight of weighted log rank test:

• "1" = unweighted.

• "n" = Gehan-Breslow.

• "sqrtN" = Tarone-Ware.

• "FH_p[a]_q[b]" = Fleming-Harrington with p=a and q=b.

approx

Approximate estimation method for Z statistics.

• "event_driven" = only work under proportional hazard model with log rank test.

• "asymptotic".

alpha

One-sided Type I error.

beta

Type II error.

ratio

Experimental:Control randomization ratio (not yet implemented).

info_frac

Targeted information fraction at each analysis.

info_scale

Information scale for calculation. Options are:

• "h0_h1_info" (default): variance under both null and alternative hypotheses is used.

• "h0_info": variance under null hypothesis is used.

• "h1_info": variance under alternative hypothesis is used.

analysis_time

Minimum time of analysis.

binding

Indicator of whether futility bound is binding; default of FALSE is recommended.

upper

Function to compute upper bound.

upar

Parameters passed to upper.

lower

Function to compute lower bound.

lpar

Parameters passed to lower.

test_upper

Indicator of which analyses should include an upper (efficacy) bound; single value of TRUE (default) indicates all analyses; otherwise, a logical vector of the same length as info should indicate which analyses will have an efficacy bound.

test_lower

Indicator of which analyses should include an lower bound; single value of TRUE (default) indicates all analyses; single value FALSE indicated no lower bound; otherwise, a logical vector of the same length as info should indicate which analyses will have a lower bound.

h1_spending

Indicator that lower bound to be set by spending under alternate hypothesis (input fail_rate) if spending is used for lower bound.

r

Integer value controlling grid for numerical integration as in Jennison and Turnbull (2000); default is 18, range is 1 to 80. Larger values provide larger number of grid points and greater accuracy. Normally, r will not be changed by the user.

tol

Tolerance parameter for boundary convergence (on Z-scale).

interval

An interval that is presumed to include the time at which expected event count is equal to targeted event.

Specification

The contents of this section are shown in PDF user manual only.

Examples

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

# set enrollment rates
enroll_rate <- define_enroll_rate(duration = 12, rate = 500 / 12)

# set failure rates
fail_rate <- define_fail_rate(
  duration = c(4, 100),
  fail_rate = log(2) / 15, # median survival 15 month
  hr = c(1, .6),
  dropout_rate = 0.001
)

# Example 1 ----
# Boundary is fixed
x <- gsSurv(
  k = 3,
  test.type = 4,
  alpha = 0.025, beta = 0.2,
  astar = 0, timing = 1,
  sfu = sfLDOF, sfupar = 0,
  sfl = sfLDOF, sflpar = 0,
  lambdaC = 0.1,
  hr = 0.6, hr0 = 1,
  eta = 0.01, gamma = 10,
  R = 12, S = NULL,
  T = 36, minfup = 24,
  ratio = 1
)

gs_design_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  ratio = 1,
  alpha = 0.025, beta = 0.2,
  weight = function(x, arm0, arm1) {
    wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = 0.5)
  },
  upper = gs_b,
  upar = x$upper$bound,
  lower = gs_b,
  lpar = x$lower$bound,
  analysis_time = c(12, 24, 36)
)

# Example 2 ----
# Boundary derived by spending function
gs_design_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  ratio = 1,
  alpha = 0.025, beta = 0.2,
  weight = function(x, arm0, arm1) {
    wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = 0.5)
  },
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2),
  analysis_time = c(12, 24, 36)
)