summary.fixed_design {gsDesign2}

Summary for fixed design or group sequential design objects

Description

Summary for fixed design or group sequential design objects

Usage

## S3 method for class 'fixed_design'
summary(object, ...)

## S3 method for class 'gs_design'
summary(
  object,
  analysis_vars = NULL,
  analysis_decimals = NULL,
  col_vars = NULL,
  col_decimals = NULL,
  bound_names = c("Efficacy", "Futility"),
  ...
)

## S3 method for class 'gs_update'
summary(
  object,
  analysis_vars = NULL,
  analysis_decimals = NULL,
  col_vars = NULL,
  col_decimals = NULL,
  bound_names = c("Efficacy", "Futility"),
  ...
)

Arguments

object

A design object returned by fixed_design_xxx() and gs_design_xxx().

...

Additional parameters (not used).

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 is c("Efficacy", "Futility").

Value

A summary table (data frame).

Examples

library(dplyr)

# Enrollment rate
enroll_rate <- define_enroll_rate(
  duration = 18,
  rate = 20
)

# Failure rates
fail_rate <- define_fail_rate(
  duration = c(4, 100),
  fail_rate = log(2) / 12,
  hr = c(1, .6),
  dropout_rate = .001
)

# Study duration in months
study_duration <- 36

# Experimental / Control randomization ratio
ratio <- 1

# 1-sided Type I error
alpha <- 0.025
# Type II error (1 - power)
beta <- 0.1

# AHR ----
# under fixed power
fixed_design_ahr(
  alpha = alpha,
  power = 1 - beta,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  study_duration = study_duration,
  ratio = ratio
) %>% summary()

# FH ----
# under fixed power
fixed_design_fh(
  alpha = alpha,
  power = 1 - beta,
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  study_duration = study_duration,
  ratio = ratio
) %>% summary()

# Design parameters ----
library(gsDesign)
library(gsDesign2)
library(dplyr)

# enrollment/failure rates
enroll_rate <- define_enroll_rate(
  stratum = "All",
  duration = 12,
  rate = 1
)
fail_rate <- define_fail_rate(
  duration = c(4, 100),
  fail_rate = log(2) / 12,
  hr = c(1, .6),
  dropout_rate = .001
)

# Information fraction
info_frac <- (1:3) / 3

# Analysis times in months; first 2 will be ignored as info_frac will not be achieved
analysis_time <- 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, analysis_time = c(12, 24, 36)),
  data.frame(rho = c(0, 0.5), gamma = 0.5, tau = -1, test = 2:3, analysis = 3, analysis_time = 36)
)

# Example 1 ----

x_ahr <- gs_design_ahr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  info_frac = info_frac, # Information fraction
  analysis_time = analysis_time,
  ratio = ratio,
  alpha = alpha,
  beta = beta,
  upper = upper,
  upar = upar,
  lower = lower,
  lpar = lpar
)

x_ahr %>% summary()

# Customize the digits to display
x_ahr %>% summary(analysis_vars = c("time", "event", "info_frac"), analysis_decimals = c(1, 0, 2))

# Customize the labels of the crossing probability
x_ahr %>% summary(bound_names = c("A is better", "B is better"))

# Customize the variables to be summarized for each analysis
x_ahr %>% summary(analysis_vars = c("n", "event"), analysis_decimals = c(1, 1))


# Example 2 ----

x_wlr <- gs_design_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  weight = wgt05,
  info_frac = NULL,
  analysis_time = sort(unique(x_ahr$analysis$time)),
  ratio = ratio,
  alpha = alpha,
  beta = beta,
  upper = upper,
  upar = upar,
  lower = lower,
  lpar = lpar
)
x_wlr %>% summary()

# Maxcombo ----

x_combo <- gs_design_combo(
  ratio = 1,
  alpha = 0.025,
  beta = 0.2,
  enroll_rate = define_enroll_rate(duration = 12, rate = 500 / 12),
  fail_rate = tibble::tibble(
    stratum = "All",
    duration = c(4, 100),
    fail_rate = log(2) / 15, hr = c(1, .6), dropout_rate = .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)
)
x_combo %>% summary()

# Risk difference ----

gs_design_rd(
  p_c = tibble::tibble(stratum = "All", rate = .2),
  p_e = tibble::tibble(stratum = "All", rate = .15),
  info_frac = c(0.7, 1),
  rd0 = 0,
  alpha = .025,
  beta = .1,
  ratio = 1,
  stratum_prev = NULL,
  weight = "unstratified",
  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()

# Design parameters ----
library(gsDesign)
library(gsDesign2)
library(dplyr)

# enrollment/failure rates
enroll_rate <- define_enroll_rate(
  stratum = "All",
  duration = 12,
  rate = 1
)
fail_rate <- define_fail_rate(
  duration = c(4, 100),
  fail_rate = log(2) / 12,
  hr = c(1, .6),
  dropout_rate = .001
)

# Information fraction
info_frac <- (1:3) / 3

# Analysis times in months; first 2 will be ignored as info_frac will not be achieved
analysis_time <- 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, analysis_time = c(12, 24, 36)),
  data.frame(rho = c(0, 0.5), gamma = 0.5, tau = -1, test = 2:3, analysis = 3, analysis_time = 36)
)

# Example 1 ----

x_ahr <- gs_design_ahr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  info_frac = info_frac, # Information fraction
  analysis_time = analysis_time,
  ratio = ratio,
  alpha = alpha,
  beta = beta,
  upper = upper,
  upar = upar,
  lower = lower,
  lpar = lpar
)

x_ahr %>% summary()

# Customize the digits to display
x_ahr %>% summary(analysis_vars = c("time", "event", "info_frac"), analysis_decimals = c(1, 0, 2))

# Customize the labels of the crossing probability
x_ahr %>% summary(bound_names = c("A is better", "B is better"))

# Customize the variables to be summarized for each analysis
x_ahr %>% summary(analysis_vars = c("n", "event"), analysis_decimals = c(1, 1))


# Example 2 ----

x_wlr <- gs_design_wlr(
  enroll_rate = enroll_rate,
  fail_rate = fail_rate,
  weight = wgt05,
  info_frac = NULL,
  analysis_time = sort(unique(x_ahr$analysis$time)),
  ratio = ratio,
  alpha = alpha,
  beta = beta,
  upper = upper,
  upar = upar,
  lower = lower,
  lpar = lpar
)
x_wlr %>% summary()

# Maxcombo ----

x_combo <- gs_design_combo(
  ratio = 1,
  alpha = 0.025,
  beta = 0.2,
  enroll_rate = define_enroll_rate(duration = 12, rate = 500 / 12),
  fail_rate = tibble::tibble(
    stratum = "All",
    duration = c(4, 100),
    fail_rate = log(2) / 15, hr = c(1, .6), dropout_rate = .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)
)
x_combo %>% summary()

# Risk difference ----

gs_design_rd(
  p_c = tibble::tibble(stratum = "All", rate = .2),
  p_e = tibble::tibble(stratum = "All", rate = .15),
  info_frac = c(0.7, 1),
  rd0 = 0,
  alpha = .025,
  beta = .1,
  ratio = 1,
  stratum_prev = NULL,
  weight = "unstratified",
  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()