Rounds sample size to an even number for equal design
Usage
to_integer(x, ...)
# S3 method for fixed_design
to_integer(x, sample_size = TRUE, ...)
# S3 method for gs_design
to_integer(x, sample_size = TRUE, ...)
Arguments
- x
An object returned by fixed_design_xxx() and gs_design_xxx().
- ...
Additional parameters (not used).
- sample_size
Logical, indicting if ceiling sample size to an even integer.
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
# \donttest{
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()
#> # A tibble: 1 × 7
#> Design N Events Time Bound alpha Power
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Average hazard ratio 464 325. 35.9 1.96 0.025 0.900
# 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()
#> # A tibble: 1 × 7
#> Design N Events Time Bound alpha Power
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Fleming-Harrington FH(0.5, 0.5) 378 264. 35.8 1.96 0.025 0.900
# 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()
#> # A tibble: 1 × 7
#> Design N Events Time Bound alpha Power
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Modestly weighted LR: tau = 4 430 302. 36.1 1.96 0.025 0.901
# }
# \donttest{
# 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()
#> # A tibble: 2 × 7
#> # Groups: Analysis [2]
#> Analysis Bound Z `~HR at bound` `Nominal p` `Alternate hypothesis`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Analysis: 1 Tim… Effi… 2.57 0.696 0.005 0.288
#> 2 Analysis: 2 Tim… Effi… 1.99 0.799 0.0234 0.901
#> # ℹ 1 more variable: `Null hypothesis` <dbl>
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()
#> # A tibble: 2 × 7
#> # Groups: Analysis [2]
#> Analysis Bound Z `~wHR at bound` `Nominal p` `Alternate hypothesis`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Analysis: 1 Ti… Effi… 2.57 0.700 0.0051 0.289
#> 2 Analysis: 2 Ti… Effi… 1.99 0.802 0.0234 0.900
#> # ℹ 1 more variable: `Null hypothesis` <dbl>
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()
#> # A tibble: 2 × 7
#> # Groups: Analysis [2]
#> Analysis Bound Z ~Risk difference at …¹ `Nominal p` `Alternate hypothesis`
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Analysi… Effi… 2.44 0.0339 0.0074 0.616
#> 2 Analysi… Effi… 2 0.0232 0.0228 0.901
#> # ℹ abbreviated name: ¹`~Risk difference at bound`
#> # ℹ 1 more variable: `Null hypothesis` <dbl>
# 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]
#> [1] 0.003808063
gsDesign::sfLDOF(alpha = 0.025, t = 18 / 30)$spend
#> [1] 0.003808063
# }