gs_design_rd.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gs_design_rd.R
\name{gs_design_rd}
\alias{gs_design_rd}
\title{Group sequential design of binary outcome measuring in risk difference}
\usage{
gs_design_rd(
  p_c = tibble::tibble(stratum = "All", rate = 0.2),
  p_e = tibble::tibble(stratum = "All", rate = 0.15),
  info_frac = 1:3/3,
  rd0 = 0,
  alpha = 0.025,
  beta = 0.1,
  ratio = 1,
  stratum_prev = NULL,
  weight = c("unstratified", "ss", "invar", "mr"),
  upper = gs_b,
  lower = gs_b,
  upar = gsDesign(k = 3, test.type = 1, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(0.1), rep(-Inf, 2)),
  test_upper = TRUE,
  test_lower = TRUE,
  info_scale = c("h0_h1_info", "h0_info", "h1_info"),
  binding = FALSE,
  r = 18,
  tol = 1e-06,
  h1_spending = TRUE
)
}
\arguments{
\item{p_c}{Rate at the control group.}

\item{p_e}{Rate at the experimental group.}

\item{info_frac}{Statistical information fraction.}

\item{rd0}{Treatment effect under super-superiority designs, the default is 0.}

\item{alpha}{One-sided Type I error.}

\item{beta}{Type II error.}

\item{ratio}{Experimental:Control randomization ratio (not yet implemented).}

\item{stratum_prev}{Randomization ratio of different stratum.
If it is unstratified design then \code{NULL}.
Otherwise it is a tibble containing two columns (stratum and prevalence).}

\item{weight}{The weighting scheme for stratified population.}

\item{upper}{Function to compute upper bound.}

\item{lower}{Function to compute lower bound.}

\item{upar}{Parameters passed to \code{upper}.}

\item{lpar}{Parameters passed to \code{lower}.}

\item{test_upper}{Indicator of which analyses should include an upper
(efficacy) bound; single value of \code{TRUE} (default) indicates all analyses;
otherwise, a logical vector of the same length as \code{info} should indicate
which analyses will have an efficacy bound.}

\item{test_lower}{Indicator of which analyses should include an lower bound;
single value of \code{TRUE} (default) indicates all analyses;
single value of \code{FALSE} indicates no lower bound; otherwise,
a logical vector of the same length as \code{info} should indicate which
analyses will have a lower bound.}

\item{info_scale}{Information scale for calculation. Options are:
\itemize{
\item \code{"h0_h1_info"} (default): variance under both null and alternative hypotheses is used.
\item \code{"h0_info"}: variance under null hypothesis is used.
\item \code{"h1_info"}: variance under alternative hypothesis is used.
}}

\item{binding}{Indicator of whether futility bound is binding;
default of \code{FALSE} is recommended.}

\item{r}{Integer value controlling grid for numerical integration
as in Jennison and Turnbull (2000); default is 18, range is 1 to 80.
Larger values provide larger number of grid points and greater accuracy.
Normally, \code{r} will not be changed by the user.}

\item{tol}{Tolerance parameter for boundary convergence (on Z-scale).}

\item{h1_spending}{Indicator that lower bound to be set by
spending under alternate hypothesis (input \code{fail_rate})
if spending is used for lower bound.}
}
\value{
A list with input parameters, analysis, and bound.
}
\description{
Group sequential design of binary outcome measuring in risk difference
}
\details{
To be added.
}
\examples{
library(gsDesign)

# Example 1 ----
# unstratified group sequential design
x <- 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(k = 2, test.type = 1, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)

y <- gs_power_rd(
  p_c = tibble::tibble(stratum = "All", rate = .2),
  p_e = tibble::tibble(stratum = "All", rate = .15),
  n = tibble::tibble(stratum = "All", n = x$analysis$n, analysis = 1:2),
  rd0 = 0,
  ratio = 1,
  weight = "unstratified",
  upper = gs_b,
  lower = gs_b,
  upar = gsDesign(k = 2, test.type = 1, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)

# The above 2 design share the same power with the same sample size and treatment effect
x$bound$probability[x$bound$bound == "upper" & x$bound$analysis == 2]
y$bound$probability[y$bound$bound == "upper" & y$bound$analysis == 2]

# Example 2 ----
# stratified group sequential design
gs_design_rd(
  p_c = tibble::tibble(
    stratum = c("biomarker positive", "biomarker negative"),
    rate = c(.2, .25)
  ),
  p_e = tibble::tibble(
    stratum = c("biomarker positive", "biomarker negative"),
    rate = c(.15, .22)
  ),
  info_frac = c(0.7, 1),
  rd0 = 0,
  alpha = .025,
  beta = .1,
  ratio = 1,
  stratum_prev = tibble::tibble(
    stratum = c("biomarker positive", "biomarker negative"),
    prevalence = c(.4, .6)
  ),
  weight = "ss",
  upper = gs_spending_bound, lower = gs_b,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  lpar = rep(-Inf, 2)
)
}