Add conditional error spending functions

DESCRIPTION

@@ -32,6 +32,7 @@ Imports:
     xtable
 Suggests:
     covr,
+    data.table,
     gridExtra,
     kableExtra,
     knitr,
@@ -43,4 +44,4 @@ Suggests:
     utils
 VignetteBuilder:
     knitr
-RoxygenNote: 7.3.1
+RoxygenNote: 7.3.2

NAMESPACE

@@ -84,6 +84,9 @@ export(sfStep)
 export(sfTDist)
 export(sfTrimmed)
 export(sfTruncated)
+export(sfXG1)
+export(sfXG2)
+export(sfXG3)
 export(simBinomial)
 export(spendingFunction)
 export(ssrCP)

R/sfXG.R

@@ -0,0 +1,203 @@
+#' Xi and Gallo conditional error spending functions
+#'
+#' Error spending functions based on Xi and Gallo (2019).
+#' The intention of these spending functions is to provide bounds where the
+#' conditional error at an efficacy bound is approximately equal to the
+#' conditional error rate for crossing the final analysis bound.
+#' This is explained in greater detail in
+#' \code{vignette("ConditionalErrorSpending")}.
+#'
+#' @param alpha Real value \eqn{> 0} and no more than 1. Normally,
+#'   \code{alpha = 0.025} for one-sided Type I error specification or
+#'   \code{alpha = 0.1} for Type II error specification.
+#'   However, this could be set to 1 if for descriptive purposes you wish
+#'   to see the proportion of spending as a function of the proportion of
+#'   sample size/information.
+#' @param t A vector of points with increasing values from 0 to 1, inclusive.
+#'   Values of the proportion of sample size/information for which the spending
+#'   function will be computed.
+#' @param param This is the gamma parameter in the Xi and Gallo
+#'   spending function paper, distinct for each function.
+#'   See the details section for functional forms and range of param
+#'   acceptable for each spending function.
+#'
+#' @return
+#' An object of type \code{spendfn}. See spending functions for
+#' further details.
+#'
+#' @details
+#' Xi and Gallo use an additive boundary for group sequential designs with
+#' connection to conditional error.
+#' Three spending functions are defined: \code{sfXG1()}, \code{sfXG2()},
+#' and \code{sfXG3()}.
+#'
+#' Method 1 is defined for \eqn{\gamma \in [0.5, 1)} as
+#'
+#' \deqn{f(Z_K \ge u_K | Z_k = u_k) = 2 - 2\times \Phi\left(\frac{z_{\alpha/2} - z_\gamma\sqrt{1-t}}{\sqrt{t}} \right).}
+#'
+#' Method 2 is defined for \eqn{\gamma \in [1 - \Phi(z_{\alpha/2}/2), 1)} as
+#'
+#' \deqn{f_\gamma(t; \alpha)=2-2\Phi \left(
+#' \Phi^{-1}(1-\alpha/2)/ t^{1/2} \right).}{% f(t;
+#' alpha)=2-2*Phi(Phi^(-1)(1-alpha/2)/t^(1/2)).}
+#'
+#' Method 3 is defined as for \eqn{\gamma \in (\alpha/2, 1)} as
+#'
+#' \deqn{f(t; \alpha)= 2 - 2\times \Phi\left(\frac{z_{\alpha/2} - z_\gamma(1-\sqrt t)}{\sqrt t} \right).}
+#'
+#' @author Keaven Anderson \email{keaven_anderson@@merck.com}
+#'
+#' \code{vignette("SpendingFunctionOverview")}, \code{\link{gsDesign}},
+#' \code{vignette("gsDesignPackageOverview")}
+#'
+#' @references
+#' Jennison C and Turnbull BW (2000), \emph{Group Sequential
+#' Methods with Applications to Clinical Trials}. Boca Raton: Chapman and Hall.
+#'
+#' Xi D and Gallo P (2019), An additive boundary for group sequential designs
+#' with connection to conditional error. \emph{Statistics in Medicine}; 38 (23),
+#' 4656--4669.
+#'
+#' @keywords design
+#'
+#' @rdname sfXG
+#'
+#' @aliases sfXG sfXG2 sfXG3
+#'
+#' @export
+#'
+#' @examples
+#' # Plot conditional error spending spending functions across
+#' # a range of values of interest
+#' pts <- seq(0, 1.2, 0.01)
+#' pal <- palette()
+#'
+#' plot(
+#'   pts,
+#'   sfXG1(0.025, pts, 0.5)$spend,
+#'   type = "l", col = pal[1],
+#'   xlab = "t", ylab = "Spending", main = "Xi-Gallo, Method 1"
+#' )
+#' lines(pts, sfXG1(0.025, pts, 0.6)$spend, col = pal[2])
+#' lines(pts, sfXG1(0.025, pts, 0.75)$spend, col = pal[3])
+#' lines(pts, sfXG1(0.025, pts, 0.9)$spend, col = pal[4])
+#' legend(
+#'   "topleft",
+#'   legend = c("gamma=0.5", "gamma=0.6", "gamma=0.75", "gamma=0.9"),
+#'   col = pal[1:4],
+#'   lty = 1
+#' )
+#'
+#' plot(
+#'   pts,
+#'   sfXG2(0.025, pts, 0.14)$spend,
+#'   type = "l", col = pal[1],
+#'   xlab = "t", ylab = "Spending", main = "Xi-Gallo, Method 2"
+#' )
+#' lines(pts, sfXG2(0.025, pts, 0.25)$spend, col = pal[2])
+#' lines(pts, sfXG2(0.025, pts, 0.5)$spend, col = pal[3])
+#' lines(pts, sfXG2(0.025, pts, 0.75)$spend, col = pal[4])
+#' lines(pts, sfXG2(0.025, pts, 0.9)$spend, col = pal[5])
+#' legend(
+#'   "topleft",
+#'   legend = c("gamma=0.14", "gamma=0.25", "gamma=0.5", "gamma=0.75", "gamma=0.9"),
+#'   col = pal[1:5],
+#'   lty = 1
+#' )
+#'
+#' plot(
+#'   pts,
+#'   sfXG3(0.025, pts, 0.013)$spend,
+#'   type = "l", col = pal[1],
+#'   xlab = "t", ylab = "Spending", main = "Xi-Gallo, Method 3"
+#' )
+#' lines(pts, sfXG3(0.025, pts, 0.02)$spend, col = pal[2])
+#' lines(pts, sfXG3(0.025, pts, 0.05)$spend, col = pal[3])
+#' lines(pts, sfXG3(0.025, pts, 0.1)$spend, col = pal[4])
+#' lines(pts, sfXG3(0.025, pts, 0.25)$spend, col = pal[5])
+#' lines(pts, sfXG3(0.025, pts, 0.5)$spend, col = pal[6])
+#' lines(pts, sfXG3(0.025, pts, 0.75)$spend, col = pal[7])
+#' lines(pts, sfXG3(0.025, pts, 0.9)$spend, col = pal[8])
+#' legend(
+#'   "bottomright",
+#'   legend = c(
+#'     "gamma=0.013", "gamma=0.02", "gamma=0.05", "gamma=0.1",
+#'     "gamma=0.25", "gamma=0.5", "gamma=0.75", "gamma=0.9"
+#'   ),
+#'   col = pal[1:8],
+#'   lty = 1
+#' )
+sfXG1 <- function(alpha, t, param) {
+  # Check for scalar parameter in [0.5, 1)
+  checkScalar(param, "numeric", c(.5, 1), c(TRUE, FALSE))
+
+  # For values of t > 1, compute value as if t = 1
+  t <- pmin(t, 1)
+
+  # Compute spending
+  y <- 2 - 2 * pnorm((qnorm(1 - alpha / 2) -
+    qnorm(1 - param) * sqrt(1 - t)) / sqrt(t))
+
+  # Assemble return value and return
+  x <- list(
+    name = "Xi-Gallo, method 1", param = param,
+    parname = "gamma", sf = sfXG1,
+    spend = y,
+    bound = NULL,
+    prob = NULL
+  )
+  class(x) <- "spendfn"
+  x
+}
+
+#' @export
+#'
+#' @rdname sfXG
+sfXG2 <- function(alpha, t, param) {
+  # Check for scalar parameter in appropriate range
+  checkScalar(param, "numeric", c(1 - pnorm(qnorm(1 - alpha / 2)), 1), c(TRUE, FALSE))
+
+  # For values of t > 1, compute value as if t = 1
+  t <- pmin(t, 1)
+
+  # Compute spending
+  y <- 2 - 2 * pnorm((qnorm(1 - alpha / 2) -
+    qnorm(1 - param) * (1 - t)) / sqrt(t))
+
+  # Assemble return value and return
+  x <- list(
+    name = "Xi-Gallo, method 2", param = param,
+    parname = "gamma", sf = sfXG2,
+    spend = y,
+    bound = NULL,
+    prob = NULL
+  )
+  class(x) <- "spendfn"
+  x
+}
+
+#' @export
+#'
+#' @rdname sfXG
+sfXG3 <- function(alpha, t, param) {
+  # Check for scalar parameter in appropriate range
+  checkScalar(param, "numeric", c(alpha / 2, 1), c(FALSE, FALSE))
+
+  # For values of t > 1, compute value as if t = 1
+  t <- pmin(t, 1)
+
+  # Compute spending
+  y <- 2 - 2 * pnorm((qnorm(1 - alpha / 2) -
+    qnorm(1 - param) * (1 - sqrt(t))) / sqrt(t))
+
+  # Assemble return value and return
+  x <- list(
+    name = "Xi-Gallo, method 1", param = param,
+    parname = "gamma", sf = sfXG3,
+    spend = y,
+    bound = NULL,
+    prob = NULL
+  )
+  class(x) <- "spendfn"
+  x
+}

_pkgdown.yml

@@ -78,6 +78,9 @@ reference:
   - sfTrimmed
   - sfTruncated
   - sfGapped
+  - sfXG1
+  - sfXG2
+  - sfXG3
 - title: Conditional and Predictive Power
   contents:
   - gsCP
@@ -143,6 +146,7 @@ articles:
       - gsDesignPackageOverview
       - SpendingFunctionOverview
       - ConditionalPowerPlot
+      - ConditionalErrorSpending
       - nNormal
       - hGraph
       - GraphicalMultiplicity