to_integer: Round sample size and events

In gsDesign2: Group Sequential Design with Non-Constant Effect

Round sample size and events

Description

Round sample size and events

Usage

to_integer(x, ...)

## S3 method for class 'fixed_design'
to_integer(x, round_up_final = TRUE, ratio = x$input$ratio, ...)

## S3 method for class 'gs_design'
to_integer(x, round_up_final = TRUE, ratio = x$input$ratio, ...)

Arguments

x	An object returned by fixed_design_xxx() and gs_design_xxx().
...	Additional parameters (not used).
round_up_final	Events at final analysis is rounded up if TRUE; otherwise, just rounded, unless it is very close to an integer.
ratio	Positive integer for randomization ratio (experimental:control). A positive integer will result in rounded sample size, which is a multiple of (ratio + 1). A positive non-integer will result in round sample size, which may not be a multiple of (ratio + 1). A negative number will result in an error.

Details

For the sample size of the fixed design:

• When ratio is a positive integer, the sample size is rounded up to a multiple of ratio + 1 if round_up_final = TRUE, and just rounded to a multiple of ratio + 1 if round_up_final = FALSE.

• When ratio is a positive non-integer, the sample size is rounded up if round_up_final = TRUE, (may not be a multiple of ratio + 1), and just rounded if round_up_final = FALSE (may not be a multiple of ratio + 1). Note the default ratio is taken from x$input$ratio.

For the number of events of the fixed design:

• If the continuous event is very close to an integer within 0.01 differences, say 100.001 or 99.999, then the integer events is 100.

• Otherwise, round up if round_up_final = TRUE and round if round_up_final = FALSE.

For the sample size of group sequential designs:

• When ratio is a positive integer, the final sample size is rounded to a multiple of ratio + 1.

  • For 1:1 randomization (experimental:control), set ratio = 1 to round to an even sample size.

  • For 2:1 randomization, set ratio = 2 to round to a multiple of 3.

  • For 3:2 randomization, set ratio = 4 to round to a multiple of 5.

  • Note that for the final analysis, the sample size is rounded up to the nearest multiple of ratio + 1 if round_up_final = TRUE. If round_up_final = FALSE, the final sample size is rounded to the nearest multiple of ratio + 1.

• When ratio is positive non-integer, the final sample size MAY NOT be rounded to a multiple of ratio + 1.

  • The final sample size is rounded up if round_up_final = TRUE.

  • Otherwise, it is just rounded.

For the events of group sequential designs:

• For events at interim analysis, it is rounded.

• For events at final analysis:

  • If the continuous event is very close to an integer within 0.01 differences, say 100.001 or 99.999, then the integer events is 100.

  • Otherwise, final events is rounded up if round_up_final = TRUE and rounded if round_up_final = FALSE.

Value

A list similar to the output of fixed_design_xxx() and gs_design_xxx(), except the sample size is an integer.

Examples

library(dplyr)
library(gsDesign2)

# Average hazard ratio

x <- fixed_design_ahr(
  alpha = .025, power = .9,
  enroll_rate = define_enroll_rate(duration = 18, rate = 1),
  fail_rate = define_fail_rate(
    duration = c(4, 100),
    fail_rate = log(2) / 12, hr = c(1, .6),
    dropout_rate = .001
  ),
  study_duration = 36
)
x |>
  to_integer() |>
  summary()

# FH
x <- fixed_design_fh(
  alpha = 0.025, power = 0.9,
  enroll_rate = define_enroll_rate(duration = 18, rate = 20),
  fail_rate = define_fail_rate(
    duration = c(4, 100),
    fail_rate = log(2) / 12,
    hr = c(1, .6),
    dropout_rate = .001
  ),
  rho = 0.5, gamma = 0.5,
  study_duration = 36, ratio = 1
)
x |>
  to_integer() |>
  summary()

# MB
x <- fixed_design_mb(
  alpha = 0.025, power = 0.9,
  enroll_rate = define_enroll_rate(duration = 18, rate = 20),
  fail_rate = define_fail_rate(
    duration = c(4, 100),
    fail_rate = log(2) / 12, hr = c(1, .6),
    dropout_rate = .001
  ),
  tau = 4,
  study_duration = 36, ratio = 1
)
x |>
  to_integer() |>
  summary()


# Example 1: Information fraction based spending
gs_design_ahr(
  analysis_time = c(18, 30),
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL),
  lower = gs_b,
  lpar = c(-Inf, -Inf)
) |>
  to_integer() |>
  summary()

gs_design_wlr(
  analysis_time = c(18, 30),
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL),
  lower = gs_b,
  lpar = c(-Inf, -Inf)
) |>
  to_integer() |>
  summary()

gs_design_rd(
  p_c = tibble::tibble(stratum = c("A", "B"), rate = c(.2, .3)),
  p_e = tibble::tibble(stratum = c("A", "B"), rate = c(.15, .27)),
  weight = "ss",
  stratum_prev = tibble::tibble(stratum = c("A", "B"), prevalence = c(.4, .6)),
  info_frac = c(0.7, 1),
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL),
  lower = gs_b,
  lpar = c(-Inf, -Inf)
) |>
  to_integer() |>
  summary()

# Example 2: Calendar based spending
x <- gs_design_ahr(
  upper = gs_spending_bound,
  analysis_time = c(18, 30),
  upar = list(
    sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL,
    timing = c(18, 30) / 30
  ),
  lower = gs_b,
  lpar = c(-Inf, -Inf)
) |> to_integer()

# The IA nominal p-value is the same as the IA alpha spending
x$bound$`nominal p`[1]
gsDesign::sfLDOF(alpha = 0.025, t = 18 / 30)$spend

gsDesign2 documentation built on April 3, 2025, 9:39 p.m.