gs_power_rd.Rd

% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gs_power_rd.R
\name{gs_power_rd}
\alias{gs_power_rd}
\title{Group sequential design power of binary outcome measuring in risk difference}
\usage{
gs_power_rd(
  p_c = tibble::tibble(stratum = "All", rate = 0.2),
  p_e = tibble::tibble(stratum = "All", rate = 0.15),
  n = tibble::tibble(stratum = "All", n = c(40, 50, 60), analysis = 1:3),
  rd0 = 0,
  ratio = 1,
  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)),
  info_scale = c("h0_h1_info", "h0_info", "h1_info"),
  binding = FALSE,
  test_upper = TRUE,
  test_lower = TRUE,
  r = 18,
  tol = 1e-06
)
}
\arguments{
\item{p_c}{Rate at the control group.}

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

\item{n}{Sample size.}

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

\item{ratio}{Experimental:control randomization ratio.}

\item{weight}{Weighting method, can be \code{"unstratified"}, \code{"ss"},
or \code{"invar"}.}

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

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

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

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

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

\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).}
}
\value{
A list with input parameter, analysis, and bound.
}
\description{
Group sequential design power of binary outcome measuring in risk difference
}
\examples{
# Example 1 ----
library(gsDesign)

# unstratified case with H0: rd0 = 0
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 = c(20, 40, 60),
    analysis = 1:3
  ),
  rd0 = 0,
  ratio = 1,
  upper = gs_b,
  lower = gs_b,
  upar = gsDesign(k = 3, test.type = 1, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)

# Example 2 ----
# unstratified case with H0: rd0 != 0
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 = c(20, 40, 60),
    analysis = 1:3
  ),
  rd0 = 0.005,
  ratio = 1,
  upper = gs_b,
  lower = gs_b,
  upar = gsDesign(k = 3, test.type = 1, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)

# use spending function
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 = c(20, 40, 60),
    analysis = 1:3
  ),
  rd0 = 0.005,
  ratio = 1,
  upper = gs_spending_bound,
  lower = gs_b,
  upar = list(sf = gsDesign::sfLDOF, total_spend = 0.025, param = NULL, timing = NULL),
  lpar = c(qnorm(.1), rep(-Inf, 2))
)

# Example 3 ----
# stratified case under sample size weighting and H0: rd0 = 0
gs_power_rd(
  p_c = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble::tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(10, 20, 24, 18, 26, 30, 10, 20, 24)
  ),
  rd0 = 0,
  ratio = 1,
  weight = "ss",
  upper = gs_b,
  lower = gs_b,
  upar = gsDesign(k = 3, test.type = 1, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)

# Example 4 ----
# stratified case under inverse variance weighting and H0: rd0 = 0
gs_power_rd(
  p_c = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble::tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(10, 20, 24, 18, 26, 30, 10, 20, 24)
  ),
  rd0 = 0,
  ratio = 1,
  weight = "invar",
  upper = gs_b,
  lower = gs_b,
  upar = gsDesign(k = 3, test.type = 1, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)

# Example 5 ----
# stratified case under sample size weighting and H0: rd0 != 0
gs_power_rd(
  p_c = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble::tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(10, 20, 24, 18, 26, 30, 10, 20, 24)
  ),
  rd0 = 0.02,
  ratio = 1,
  weight = "ss",
  upper = gs_b,
  lower = gs_b,
  upar = gsDesign(k = 3, test.type = 1, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)

# Example 6 ----
# stratified case under inverse variance weighting and H0: rd0 != 0
gs_power_rd(
  p_c = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.15, .2, .25)
  ),
  p_e = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rate = c(.1, .16, .19)
  ),
  n = tibble::tibble(
    stratum = rep(c("S1", "S2", "S3"), each = 3),
    analysis = rep(1:3, 3),
    n = c(10, 20, 24, 18, 26, 30, 10, 20, 24)
  ),
  rd0 = 0.03,
  ratio = 1,
  weight = "invar",
  upper = gs_b,
  lower = gs_b,
  upar = gsDesign(k = 3, test.type = 1, sfu = sfLDOF, sfupar = NULL)$upper$bound,
  lpar = c(qnorm(.1), rep(-Inf, 2))
)
}