gs_power_ahr: Group sequential design power using average hazard ratio...

In keaven/gsDesign2: Group Sequential Design with Non-Constant Effect

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

Description

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

Usage

gs_power_ahr(
  enrollRates = tibble(Stratum = "All", duration = c(2, 2, 10), rate = c(3, 6, 9)),
  failRates = tibble(Stratum = "All", duration = c(3, 100), failRate = log(2)/c(9, 18),
    hr = c(0.9, 0.6), dropoutRate = rep(0.001, 2)),
  events = c(30, 40, 50),
  analysisTimes = NULL,
  upper = gs_b,
  upar = gsDesign(k = length(events), test.type = 1, n.I = events, maxn.IPlan =
    max(events), sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lower = gs_b,
  lpar = c(qnorm(0.1), rep(-Inf, 2)),
  test_lower = TRUE,
  test_upper = TRUE,
  ratio = 1,
  binding = FALSE,
  info_scale = c(0, 1, 2),
  r = 18,
  tol = 1e-06
)

Arguments

enrollRates	enrollment rates
failRates	failure and dropout rates
events	Targeted events at each analysis
analysisTimes	Minimum time of analysis
upper	Function to compute upper bound
upar	Parameter passed to upper()
lower	Function to compute lower bound
lpar	Parameter passed to lower()
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 (not yet implemented)
binding	indicator of whether futility bound is binding; default of FALSE is recommended
info_scale	the information scale for calculation
r	Integer, at least 2; default of 18 recommended by Jennison and Turnbull
tol	Tolerance parameter for boundary convergence (on Z-scale)

Details

Bound satisfy input upper bound specification in upper, upar and lower bound specification in lower, lpar. The AHR() function computes statistical information at targeted event times. The tEvents() function is used to get events and average HR at targeted analysisTimes.

Value

a tibble with columns Analysis, Bound, Z, Probability, theta, Time, AHR, Events. Contains a row for each analysis and each bound.

Specification

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

Examples

library(gsDesign2)
library(dplyr)

# -------------------------#
#       example 1          #
# ------------------------ #
# The default output of \code{gs_power_ahr} is driven by events, i.e.,
# \code{events = c(30, 40, 50), analysisTimes = NULL}
gs_power_ahr() 

# -------------------------#
#       example 2          #
# -------------------------#
# 2-sided symmetric O'Brien-Fleming spending bound, 
# driven by analysis time, i.e., \code{events = NULL, analysisTimes = c(12, 24, 36)}
gs_power_ahr(
  analysisTimes = c(12, 24, 36),
  events = NULL,
  binding = TRUE,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL)) 

# -------------------------#
#       example 3          #
# -------------------------#
# 2-sided symmetric O'Brien-Fleming spending bound,
# driven by events, i.e., \code{events = c(20, 50, 70), analysisTimes = NULL}
gs_power_ahr(
  analysisTimes = NULL,
  events = c(20, 50, 70),
  binding = TRUE,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL))

# -------------------------#
#       example 4          #
# -------------------------#
# 2-sided symmetric O'Brien-Fleming spending bound,
# driven by both `events` and `analysisTimes`, i.e.,
# both `events` and `analysisTimes` are not `NULL`,
# then the analysis will driven by the maximal one, i.e.,
# Time = max(analysisTime, calculated Time for targeted events)
# Events = max(events, calculated events for targeted analysisTime)
gs_power_ahr(
  analysisTimes = c(12, 24, 36),
  events = c(30, 40, 50),
  binding = TRUE,
  upper = gs_spending_bound,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  lower = gs_spending_bound,
  lpar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL))
  

keaven/gsDesign2 documentation built on Oct. 13, 2022, 8:42 p.m.