## ----setup, include = FALSE---------------------------------------------------
knitr::opts_chunk$set(
  collapse   = TRUE,
  comment    = "#>",
  fig.width  = 7,
  fig.height = 4.5,
  out.width  = "100%",
  dpi        = 96
)
library(SingleArmMRCT)

## -----------------------------------------------------------------------------
result_f <- rcp1armContinuous(
  mu       = 0.5,
  mu0      = 0.1,
  sd       = 1,
  Nj       = c(20, 40, 40),
  PI       = 0.5,
  approach = "formula"
)
print(result_f)

## -----------------------------------------------------------------------------
result_s <- rcp1armContinuous(
  mu       = 0.5,
  mu0      = 0.1,
  sd       = 1,
  Nj       = c(20, 40, 40),
  PI       = 0.5,
  approach = "simulation",
  nsim     = 10000,
  seed     = 1
)
print(result_s)

## ----fig.alt="Line plot of RCP versus f1 for a continuous endpoint with mu = 0.5, mu0 = 0.1, sigma = 1, showing Method 1 and Method 2 across N = 20, 40, 100"----
plot_rcp1armContinuous(
  mu        = 0.5,
  mu0       = 0.1,
  sd        = 1,
  PI        = 0.5,
  N_vec     = c(20, 40, 100),
  J         = 3,
  nsim      = 5000,
  seed      = 1,
  base_size = 8
)

## -----------------------------------------------------------------------------
result_f <- rcp1armBinary(
  p        = 0.5,
  p0       = 0.2,
  Nj       = c(20, 40, 40),
  PI       = 0.5,
  approach = "formula"
)
print(result_f)

## -----------------------------------------------------------------------------
result_s <- rcp1armBinary(
  p        = 0.5,
  p0       = 0.2,
  Nj       = c(20, 40, 40),
  PI       = 0.5,
  approach = "simulation",
  nsim     = 10000,
  seed     = 1
)
print(result_s)

## ----fig.alt="Line plot of RCP versus f1 for a binary endpoint with p = 0.5, p0 = 0.2, showing Method 1 and Method 2 across N = 20, 40, 100"----
plot_rcp1armBinary(
  p         = 0.5,
  p0        = 0.2,
  PI        = 0.5,
  N_vec     = c(20, 40, 100),
  J         = 3,
  nsim      = 5000,
  seed      = 1,
  base_size = 8
)

## -----------------------------------------------------------------------------
result_f <- rcp1armCount(
  lambda     = 2,
  lambda0    = 3,
  dispersion = 1,
  Nj         = c(20, 40, 40),
  PI         = 0.5,
  approach   = "formula"
)
print(result_f)

## -----------------------------------------------------------------------------
result_s <- rcp1armCount(
  lambda     = 2,
  lambda0    = 3,
  dispersion = 1,
  Nj         = c(20, 40, 40),
  PI         = 0.5,
  approach   = "simulation",
  nsim       = 10000,
  seed       = 1
)
print(result_s)

## ----fig.height=6, fig.alt="Grid plot of RCP versus f1 for a count endpoint with lambda = 2, lambda0 = 3, showing Method 1 on log-RR and linear-RR scales and Method 2 across N = 20, 40, 100"----
plot_rcp1armCount(
  lambda     = 2,
  lambda0    = 3,
  dispersion = 1,
  PI         = 0.5,
  N_vec      = c(20, 40, 100),
  J          = 3,
  nsim       = 5000,
  seed       = 1,
  base_size  = 11
)

## ----echo=FALSE---------------------------------------------------------------
tbl <- data.frame(
  Endpoint   = c("Continuous", "Binary", "Count"),
  Model      = c("Normal", "Binomial", "Negative binomial"),
  `Effect parameter` = c(
    "$\\delta = \\mu - \\mu_0$",
    "$\\delta = p - p_0$",
    "$\\log(RR) = \\log(\\lambda/\\lambda_0)$ (Method 1, log-RR scale); $1 - RR = 1 - \\lambda/\\lambda_0$ (Method 1, linear-RR scale)"
  ),
  `Benefit direction` = c(
    "$\\hat{\\mu}_j > \\mu_0$",
    "$\\hat{p}_j > p_0$",
    "$\\widehat{RR}_j < 1$"
  ),
  `Method 1 computation` = c(
    "Closed-form (normal approximation)",
    "Exact enumeration (binomial)",
    "Exact enumeration (negative binomial)"
  ),
  `Method 2 computation` = c(
    "Product of normal tail probabilities",
    "Product of binomial tail probabilities",
    "Product of NB tail probabilities"
  ),
  check.names = FALSE
)
knitr::kable(tbl, align = "llllll")