gsDesign2 (version 1.1.5)

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

Description

Calculate power given the sample size in group sequential design power using average hazard ratio under non-proportional hazards.

Usage

gs_power_ahr(
  enroll_rate = define_enroll_rate(duration = c(2, 2, 10), rate = c(3, 6, 9)),
  fail_rate = define_fail_rate(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,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = NULL),
  test_lower = TRUE,
  test_upper = TRUE,
  ratio = 1,
  binding = FALSE,
  h1_spending = TRUE,
  info_scale = c("h0_h1_info", "h0_info", "h1_info"),
  r = 18,
  tol = 1e-06,
  interval = c(0.01, 1000),
  integer = FALSE
)

Value

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

• $input a list including alpha, beta, ratio, etc.

• $enroll_rate a table showing the enrollment, which is the same as input.

• $fail_rate a table showing the failure and dropout rates, which is the same as input.

• $bound a table summarizing the efficacy and futility bound at each analysis.

• analysis a table summarizing the analysis time, sample size, events, average HR, treatment effect and statistical information at each analysis.

Arguments

enroll_rate

Enrollment rates defined by define_enroll_rate().

fail_rate

Failure and dropout rates defined by define_fail_rate().

event

A numerical vector specifying the targeted events at each analysis. See details.

analysis_time

Targeted calendar timing of analyses. See details.

upper

Function to compute upper bound.

• gs_spending_bound(): alpha-spending efficacy bounds.

• gs_b(): fixed efficacy bounds.

upar

Parameters passed to upper.

• If upper = gs_b, then upar is a numerical vector specifying the fixed efficacy bounds per analysis.

• If upper = gs_spending_bound, then upar is a list including

  • sf for the spending function family.

  • total_spend for total alpha spend.

  • param for the parameter of the spending function.

  • timing specifies spending time if different from information-based spending; see details.

lower

Function to compute lower bound, which can be set up similarly as upper. See this vignette.

lpar

Parameters passed to lower, which can be set up similarly as upar.

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.

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.

ratio

Experimental:Control randomization ratio.

binding

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

h1_spending

Indicator that lower bound to be set by spending under alternate hypothesis (input fail_rate) if spending is used for lower bound. If this is FALSE, then the lower bound spending is under the null hypothesis. This is for two-sided symmetric or asymmetric testing under the null hypothesis; See this vignette.

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.

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); normally not changed by the user.

interval

An interval presumed to include the times at which expected event count is equal to targeted event. Normally, this can be ignored by the user as it is set to c(.01, 1000).

integer

Indicator of whether integer sample size and events are intended. This argument is used when using to_integer().

Specification

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

Details

Note that time units are arbitrary, but should be the same for all rate parameters in enroll_rate, fail_rate, and analysis_time.

Computed bounds satisfy input upper bound specification in upper, upar, and lower bound specification in lower, lpar. ahr() computes statistical information at targeted event times. The expected_time() function is used to get events and average HR at targeted analysis_time.

The parameters event and analysis_time are used to determine the timing for interim and final analyses.

• If analysis timing is to be determined by targeted events, then event is a numerical vector specifying the targeted events for each analysis; note that this can be NULL.

• If interim analysis is determined by targeted calendar timing relative to start of enrollment, then analysis_time will be a vector specifying the calendar time from start of study for each analysis; note that this can be NULL.

• A corresponding element of event or analysis_time should be provided for each analysis.

• If both event[i] and analysis[i] are provided for analysis i, then the time corresponding to the later of these is used for analysis i.

Examples

library(gsDesign2)
library(dplyr)

# Example 1 ----
# The default output of `gs_power_ahr()` is driven by events,
# i.e., `event = c(30, 40, 50)`, `analysis_time = NULL`
# \donttest{
gs_power_ahr(lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.1))
# }
# Example 2 ----
# 2-sided symmetric O'Brien-Fleming spending bound, driven by analysis time,
# i.e., `event = NULL`, `analysis_time = c(12, 24, 36)`

gs_power_ahr(
  analysis_time = c(12, 24, 36),
  event = NULL,
  binding = TRUE,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025)
)

# Example 3 ----
# 2-sided symmetric O'Brien-Fleming spending bound, driven by event,
# i.e., `event = c(20, 50, 70)`, `analysis_time = NULL`
# Note that this assumes targeted final events for the design is 70 events.
# If actual targeted final events were 65, then `timing = c(20, 50, 70) / 65`
# would be added to `upar` and `lpar` lists.
# NOTE: at present the computed information fraction in output `analysis` is based
# on 70 events rather than 65 events when the `timing` argument is used in this way.
# A vignette on this topic will be forthcoming.
# \donttest{
gs_power_ahr(
  analysis_time = NULL,
  event = c(20, 50, 70),
  binding = TRUE,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025)
)
# }
# Example 4 ----
# 2-sided symmetric O'Brien-Fleming spending bound,
# driven by both `event` and `analysis_time`, i.e.,
# both `event` and `analysis_time` are not `NULL`,
# then the analysis will driven by the maximal one, i.e.,
# Time = max(analysis_time, calculated Time for targeted event)
# Events = max(events, calculated events for targeted analysis_time)
# \donttest{
gs_power_ahr(
  analysis_time = c(12, 24, 36),
  event = c(30, 40, 50), h1_spending = FALSE,
  binding = TRUE,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025)
)
# }