gsDesign2/man/gs_info_rd.Rd at 09bf586aaaa893d32dcfe5e050237afc47923f28 · Merck/gsDesign2 · GitHub

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% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/gs_info_rd.R
\name{gs_info_rd}
\alias{gs_info_rd}
\title{Information and effect size under risk difference}
\usage{
gs_info_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(100, 200, 300), analysis = 1:3),
  rd0 = 0,
  ratio = 1,
  weight = c("unstratified", "ss", "invar", "mr")
)
}
\arguments{
\item{p_c}{Rate at the control group.}

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

\item{n}{Sample size.}

\item{rd0}{The risk difference under H0.}

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

\item{weight}{Weighting method, can be \code{"unstratified"}, \code{"ss"},
or \code{"invar"}.}
}
\value{
A tibble with columns as analysis index, sample size,
risk difference, risk difference under null hypothesis, theta1
(standardized treatment effect under alternative hypothesis),
theta0 (standardized treatment effect under null hypothesis),
and statistical information.
}
\description{
Information and effect size under risk difference
}
\examples{
# Example 1 ----
# unstratified case with H0: rd0 = 0
gs_info_rd(
  p_c = tibble::tibble(stratum = "All", rate = .15),
  p_e = tibble::tibble(stratum = "All", rate = .1),
  n = tibble::tibble(stratum = "All", n = c(100, 200, 300), analysis = 1:3),
  rd0 = 0,
  ratio = 1
)

# Example 2 ----
# unstratified case with H0: rd0 != 0
gs_info_rd(
  p_c = tibble::tibble(stratum = "All", rate = .2),
  p_e = tibble::tibble(stratum = "All", rate = .15),
  n = tibble::tibble(stratum = "All", n = c(100, 200, 300), analysis = 1:3),
  rd0 = 0.005,
  ratio = 1
)

# Example 3 ----
# stratified case under sample size weighting and H0: rd0 = 0
gs_info_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(50, 100, 200, 40, 80, 160, 60, 120, 240)
  ),
  rd0 = 0,
  ratio = 1,
  weight = "ss"
)

# Example 4 ----
# stratified case under inverse variance weighting and H0: rd0 = 0
gs_info_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(50, 100, 200, 40, 80, 160, 60, 120, 240)
  ),
  rd0 = 0,
  ratio = 1,
  weight = "invar"
)

# Example 5 ----
# stratified case under minimal risk weighting and H0: rd0 = 0
gs_info_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(50, 100, 200, 40, 80, 160, 60, 120, 240)
  ),
  rd0 = 0,
  ratio = 1,
  weight = "mr"
)

# Example 6 ----
# stratified case under sample size weighting and H0: rd0 != 0
gs_info_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(50, 100, 200, 40, 80, 160, 60, 120, 240)
  ),
  rd0 = 0.02,
  ratio = 1,
  # users can switch to either "invar" or "mr" weighting as well
  weight = "ss"
)

# Example 7 ----
# stratified case under inverse variance weighting and H0: rd0 != 0 and
# rd0 difference for different stratum
gs_info_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(50, 100, 200, 40, 80, 160, 60, 120, 240)
  ),
  rd0 = tibble::tibble(
    stratum = c("S1", "S2", "S3"),
    rd0 = c(0.01, 0.02, 0.03)
  ),
  ratio = 1,
  weight = "invar"
)
}