This is the function to generate a R table summarizing the bounds
in the group sequential design generated by
gs_design_ahr or gs_design_wlr or gs_design_combo.
Source: R/summary.R
summary.gs_design.RdThis is the function to generate a R table summarizing the bounds
in the group sequential design generated by
gs_design_ahr or gs_design_wlr or gs_design_combo.
Arguments
- x
an object returned by
gs_design_ahrorgs_design_wlrorgs_design_combo- analysis_vars
the variables to be put at the summary header of each analysis
- analysis_decimals
the displayed number of digits of
analysis_vars- col_vars
the variables to be displayed
- col_decimals
the decimals to be displayed for the displayed variables in
col_vars- bound_names
names for bounds; default = c("Efficacy", "Futility").
- ...
additional arguments
Examples
# ---------------------------- #
# design parameters #
# ---------------------------- #
library(tibble)
library(gsDesign)
library(gsDesign2)
library(dplyr)
# enrollment/failure rates
enrollRates <- tibble(Stratum = "All",
duration = 12,
rate = 1)
failRates <- tibble(Stratum = "All", duration = c(4, 100),
failRate = log(2) / 12,
hr = c(1, .6),
dropoutRate = .001)
# Information fraction
IF <- (1:3)/3
# Analysis times in months; first 2 will be ignored as IF will not be achieved
analysisTimes <- c(.01, .02, 36)
# Experimental / Control randomization ratio
ratio <- 1
# 1-sided Type I error
alpha <- 0.025
# Type II error (1 - power)
beta <- .1
# Upper bound
upper <- gs_spending_bound
upar <- list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL)
# Lower bound
lower <- gs_spending_bound
lpar <- list(sf = gsDesign::sfHSD, total_spend = 0.1, param = 0, timing = NULL)
# weight function in WLR
wgt00 <- function(x, arm0, arm1){wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = 0)}
wgt05 <- function(x, arm0, arm1){wlr_weight_fh(x, arm0, arm1, rho = 0, gamma = .5)}
# test in COMBO
fh_test <- rbind(
data.frame(rho = 0, gamma = 0, tau = -1, test = 1, Analysis = 1:3,analysisTimes = c(12, 24, 36)),
data.frame(rho = c(0, 0.5), gamma = 0.5, tau = -1, test = 2:3, Analysis = 3, analysisTimes = 36)
)
# ---------------------------- #
# ahr #
# ---------------------------- #
x_ahr <- gs_design_ahr(
enrollRates = enrollRates,
failRates = failRates,
IF = IF, # Information fraction
analysisTimes = analysisTimes,
ratio = ratio,
alpha = alpha,
beta = beta,
upper = upper,
upar = upar,
lower = lower,
lpar = lpar)
x_ahr %>% summary()
#> # A tibble: 6 × 7
#> # Groups: Analysis [3]
#> Analysis Bound Z ~HR a…¹ Nomin…² Alter…³ Null …⁴
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Analysis: 1 Time: 11.7 N: 479.6 E… Futi… -0.94 1.19 0.826 0.0338 0.176
#> 2 Analysis: 1 Time: 11.7 N: 479.6 E… Effi… 3.71 0.510 0.0001 0.0027 0.0001
#> 3 Analysis: 2 Time: 20.3 N: 493.1 E… Futi… 0.63 0.923 0.266 0.0666 0.739
#> 4 Analysis: 2 Time: 20.3 N: 493.1 E… Effi… 2.51 0.725 0.006 0.414 0.0061
#> 5 Analysis: 3 Time: 36 N: 493.1 Eve… Futi… 1.99 0.812 0.0233 0.101 0.977
#> 6 Analysis: 3 Time: 36 N: 493.1 Eve… Effi… 1.99 0.812 0.0231 0.9 0.0246
#> # … with abbreviated variable names ¹`~HR at bound`, ²`Nominal p`,
#> # ³`Alternate hypothesis`, ⁴`Null hypothesis`
x_ahr %>% summary(analysis_vars = c("Time", "Events", "IF"), analysis_decimals = c(1, 0, 2))
#> # A tibble: 6 × 7
#> # Groups: Analysis [3]
#> Analysis Bound Z ~HR a…¹ Nomin…² Alter…³ Null …⁴
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Analysis: 1 Time: 11.7 Events: 12… Futi… -0.94 1.19 0.826 0.0338 0.176
#> 2 Analysis: 1 Time: 11.7 Events: 12… Effi… 3.71 0.510 0.0001 0.0027 0.0001
#> 3 Analysis: 2 Time: 20.3 Events: 24… Futi… 0.63 0.923 0.266 0.0666 0.739
#> 4 Analysis: 2 Time: 20.3 Events: 24… Effi… 2.51 0.725 0.006 0.414 0.0061
#> 5 Analysis: 3 Time: 36 Events: 365 … Futi… 1.99 0.812 0.0233 0.101 0.977
#> 6 Analysis: 3 Time: 36 Events: 365 … Effi… 1.99 0.812 0.0231 0.9 0.0246
#> # … with abbreviated variable names ¹`~HR at bound`, ²`Nominal p`,
#> # ³`Alternate hypothesis`, ⁴`Null hypothesis`
x_ahr %>% summary(bound_names = c("A is better", "B is better"))
#> # A tibble: 6 × 7
#> # Groups: Analysis [3]
#> Analysis Bound Z ~HR a…¹ Nomin…² Alter…³ Null …⁴
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Analysis: 1 Time: 11.7 N: 479.6 E… B is… -0.94 1.19 0.826 0.0338 0.176
#> 2 Analysis: 1 Time: 11.7 N: 479.6 E… A is… 3.71 0.510 0.0001 0.0027 0.0001
#> 3 Analysis: 2 Time: 20.3 N: 493.1 E… B is… 0.63 0.923 0.266 0.0666 0.739
#> 4 Analysis: 2 Time: 20.3 N: 493.1 E… A is… 2.51 0.725 0.006 0.414 0.0061
#> 5 Analysis: 3 Time: 36 N: 493.1 Eve… B is… 1.99 0.812 0.0233 0.101 0.977
#> 6 Analysis: 3 Time: 36 N: 493.1 Eve… A is… 1.99 0.812 0.0231 0.9 0.0246
#> # … with abbreviated variable names ¹`~HR at bound`, ²`Nominal p`,
#> # ³`Alternate hypothesis`, ⁴`Null hypothesis`
# ---------------------------- #
# wlr #
# ---------------------------- #
x_wlr <- gs_design_wlr(
enrollRates = enrollRates,
failRates = failRates,
weight = wgt05,
IF = NULL,
analysisTimes = sort(unique(x_ahr$analysis$Time)),
ratio = ratio,
alpha = alpha,
beta = beta,
upper = upper,
upar = upar,
lower = lower,
lpar = lpar
)
x_wlr %>% summary()
#> # A tibble: 6 × 7
#> # Groups: Analysis [3]
#> Analysis Bound Z ~wHR …¹ Nomin…² Alter…³ Null …⁴
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Analysis: 1 Time: 11.7 N: 361.4 E… Futi… -1.17 1.28 0.879 0.0141 0.122
#> 2 Analysis: 1 Time: 11.7 N: 361.4 E… Effi… 6.02 0.284 0 0 0
#> 3 Analysis: 2 Time: 20.3 N: 371.6 E… Futi… 0.57 0.919 0.283 0.0464 0.723
#> 4 Analysis: 2 Time: 20.3 N: 371.6 E… Effi… 3.16 0.627 0.0008 0.214 0.0008
#> 5 Analysis: 3 Time: 36 N: 371.6 Eve… Futi… 1.96 0.789 0.0247 0.100 0.978
#> 6 Analysis: 3 Time: 36 N: 371.6 Eve… Effi… 1.96 0.789 0.0247 0.9 0.0225
#> # … with abbreviated variable names ¹`~wHR at bound`, ²`Nominal p`,
#> # ³`Alternate hypothesis`, ⁴`Null hypothesis`
# ---------------------------- #
# max combo #
# ---------------------------- #
x_combo <- gs_design_combo(
ratio = 1,
alpha = 0.025,
beta = 0.2,
enrollRates = tibble::tibble(Stratum = "All", duration = 12, rate = 500/12),
failRates = tibble::tibble(Stratum = "All", duration = c(4, 100),
failRate = log(2) / 15, hr = c(1, .6), dropoutRate = .001),
fh_test = fh_test,
upper = gs_spending_combo,
upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025),
lower = gs_spending_combo,
lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.2))
#> The AHR reported in the `analysis` table is under the log-rank test.
x_combo %>% summary()
#> # A tibble: 6 × 6
#> # Groups: Analysis [3]
#> Analysis Bound Z Nomin…¹ Alter…² Null …³
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 Analysis: 1 Time: 12 N: 301.3 Events: 64.… Futi… -2.72 0.997 0.0003 0
#> 2 Analysis: 1 Time: 12 N: 301.3 Events: 64.… Effi… 6.18 0 0 0
#> 3 Analysis: 2 Time: 24 N: 301.3 Events: 148… Futi… 0.65 0.257 0.0847 0
#> 4 Analysis: 2 Time: 24 N: 301.3 Events: 148… Effi… 2.8 0.0026 0.220 0.0026
#> 5 Analysis: 3 Time: 36 N: 301.3 Events: 199… Futi… 2.1 0.018 0.2 0
#> 6 Analysis: 3 Time: 36 N: 301.3 Events: 199… Effi… 2.1 0.018 0.8 0.025
#> # … with abbreviated variable names ¹`Nominal p`, ²`Alternate hypothesis`,
#> # ³`Null hypothesis`
# ---------------------------- #
# risk difference #
# ---------------------------- #
gs_design_rd(
p_c = tibble(Stratum = "All", Rate = .2),
p_e = tibble(Stratum = "All", Rate = .15),
IF = c(0.7, 1),
rd0 = 0,
alpha = .025,
beta = .1,
ratio = 1,
stratum_prev = NULL,
weight = "un-stratified",
upper = gs_b,
lower = gs_b,
upar = gsDesign::gsDesign(k = 3, test.type = 1, sfu = gsDesign::sfLDOF, sfupar = NULL)$upper$bound,
lpar = c(qnorm(.1), rep(-Inf, 2))
) %>% summary()
#> # A tibble: 4 × 7
#> # Groups: Analysis [2]
#> Analysis Bound Z ~Risk d…¹ Nomin…² Alter…³ Null …⁴
#> <chr> <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 Analysis: 1 N: 2335.7 risk di… Futi… -1.28 -0.0201 0.9 0 0.0995
#> 2 Analysis: 1 N: 2335.7 risk di… Effi… 3.71 0.0582 0.0001 0.298 0.0001
#> 3 Analysis: 2 N: 3336.7 risk di… Futi… -Inf -Inf 1 0 0.0995
#> 4 Analysis: 2 N: 3336.7 risk di… Effi… 2.51 0.033 0.006 0.9 0.006
#> # … with abbreviated variable names ¹`~Risk difference at bound`, ²`Nominal p`,
#> # ³`Alternate hypothesis`, ⁴`Null hypothesis`