gs_power_wlr {gsDesign2}

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

Description

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

Usage

gs_power_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)),
  event = c(30, 40, 50),
  analysis_time = NULL,
  binding = FALSE,
  upper = gs_b,
  lower = gs_b,
  upar = gsDesign(k = 3, test.type = 1, n.I = c(30, 40, 50), maxn.IPlan = 50, sfu =
    sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(0.1), rep(-Inf, 2)),
  test_upper = TRUE,
  test_lower = TRUE,
  ratio = 1,
  weight = wlr_weight_fh,
  info_scale = c("h0_h1_info", "h0_info", "h1_info"),
  approx = "asymptotic",
  r = 18,
  tol = 1e-06,
  interval = c(0.01, 1000)
)

Arguments

enroll_rate

Enrollment rates.

fail_rate

Failure and dropout rates.

event

Targeted event at each analysis.

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.

lower

Function to compute lower bound.

upar

Parameters passed to upper.

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.

ratio

Experimental:Control randomization ratio (not yet implemented).

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.

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.

approx

Approximate estimation method for Z statistics.

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

• "asymptotic".

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.

Value

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

Specification

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

Examples

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
)

# set the targeted number of events and analysis time
target_events <- c(30, 40, 50)
target_analysisTime <- c(10, 24, 30)

# Example 1 ----

# fixed bounds and calculate the power for targeted number of events
gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = target_events,
  analysis_time = NULL,
  upper = gs_b,
  upar = gsDesign(
    k = length(target_events),
    test.type = 1,
    n.I = target_events,
    maxn.IPlan = max(target_events),
    sfu = sfLDOF,
    sfupar = NULL
  )$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)

# Example 2 ----
# fixed bounds and calculate the power for targeted analysis time

gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = NULL,
  analysis_time = target_analysisTime,
  upper = gs_b,
  upar = gsDesign(
    k = length(target_events),
    test.type = 1,
    n.I = target_events,
    maxn.IPlan = max(target_events),
    sfu = sfLDOF,
    sfupar = NULL
  )$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)

# Example 3 ----
# fixed bounds and calculate the power for targeted analysis time & number of events

gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = target_events,
  analysis_time = target_analysisTime,
  upper = gs_b,
  upar = gsDesign(
    k = length(target_events),
    test.type = 1,
    n.I = target_events,
    maxn.IPlan = max(target_events),
    sfu = sfLDOF,
    sfupar = NULL
  )$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)

# Example 4 ----
# spending bounds and calculate the power for targeted number of events

gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = target_events,
  analysis_time = NULL,
  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)
)

# Example 5 ----
# spending bounds and calculate the power for targeted analysis time

gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = NULL,
  analysis_time = target_analysisTime,
  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)
)

# Example 6 ----
# spending bounds and calculate the power for targeted analysis time & number of events

gs_power_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  event = target_events,
  analysis_time = target_analysisTime,
  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)
)