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plot.multiarm_des_gs_norm.R
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plot.multiarm_des_gs_norm.R
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#' Plot operating characteristics of a multi-stage group-sequential multi-arm
#' clinical trial for a normally distributed primary outcome
#'
#' \code{plot.multiarm_des_gs_norm()} produces power curve plots for a specified
#' multi-stage group-sequential multi-arm clinical trial design assuming the
#' primary outcome is normally distributed.
#'
#' @param x A \code{\link{list}} of class \code{"multiarm_des_gs_norm"}, as
#' returned by \code{\link{build_gs_norm}} or \code{\link{des_gs_norm}} (i.e., a
#' multi-stage group-sequential multi-arm clinical trial design for a normally
#' distributed outcome). Defaults to \code{des_gs_norm()}.
#' @param delta_min A \code{\link{numeric}} specifying the chosen minimum value
#' for the treatment effects to include on the produced plots. Defaults to
#' \code{-x$delta1}.
#' @param delta_max A \code{\link{numeric}} specifying the chosen maximum
#' value for the treatment effects to include on the produced plots. Defaults to
#' \code{2*x$delta1}.
#' @param delta A \code{\link{numeric}} specifying the chosen treatment effect
#' shift to use in the 'shifted treatment effects plot'. Defaults to
#' \code{x$delta1 - x$delta0}.
#' @param density A \code{\link{numeric}} variable indicating the number of
#' treatment effect scenarios to consider for each power curve. Increasing
#' \code{density} increases the smoothness of the produced plots, at the cost of
#' increased run time. Defaults to \code{100}.
#' @param output A \code{\link{logical}} variable indicating whether the
#' available outputs from the function (see below) should be returned. Defaults
#' to \code{FALSE}.
#' @param print_plots A \code{\link{logical}} variable indicating whether to
#' print produced plots. Defaults to \code{TRUE}.
#' @param summary A \code{\link{logical}} variable indicating whether a summary
#' of the function's progress should be printed to the console. Defaults to
#' \code{FALSE}.
#' @param ... Not currently used.
#' @return If \code{output = T}, a list containing the following elements
#' \itemize{
#' \item A \code{\link{list}} in the slot \code{$plots} containing the produced
#' plots.
#' \item Each of the input variables.
#' }
#' @examples
#' \dontrun{
#' # The design for the default parameters
#' des <- des_gs_norm()
#' plot(des)
#' }
#' @seealso \code{\link{build_gs_norm}}, \code{\link{des_gs_norm}},
#' \code{\link{gui}}, \code{\link{opchar_gs_norm}}, \code{\link{sim_gs_norm}}.
#' @method plot multiarm_des_gs_norm
#' @export
plot.multiarm_des_gs_norm <- function(x = des_gs_norm(), delta_min = -x$delta1,
delta_max = 2*x$delta1,
delta = x$delta1 - x$delta0,
density = 100, output = FALSE,
print_plots = TRUE,
summary = FALSE, ...) {
##### Check input variables ##################################################
#check_multiarm_des_gs_norm(x, name = "x")
check_real_range_strict(delta_min, "delta_min", c(-Inf, Inf), 1)
check_real_range_strict(delta_max, "delta_max", c(-Inf, Inf), 1)
if (delta_min >= delta_max) {
stop("delta_min must be strictly less than delta_max")
}
check_real_range_strict(delta, "delta", c(0, Inf), 1)
check_integer_range(density, "density", c(0, Inf), 1)
check_logical(output, "output")
check_logical(summary, "summary")
##### Print summary ##########################################################
if (summary) {
#summary_plot_multiarm_des_gs(x, delta_min, delta_max, delta, density,
# "norm")
message("")
}
##### Perform main computations ##############################################
if (all(summary, output)) {
message(" Beginning production of plots with equal treatment effects..")
}
K <- x$K
alpha <- x$alpha
beta <- x$beta
delta0 <- x$delta0
delta1 <- x$delta1
seq_K <- 1:K
plots <- list()
boundaries <-
tibble::tibble(Stage = rep(1:x$J, 2),
Type = rep(c("Efficacy", "Futility"),
each = x$J),
`Stopping boundary` = c(x$e, x$f))
plots$boundaries <-
ggplot2::ggplot(boundaries,
ggplot2::aes(x = .data$Stage,
y = .data$`Stopping boundary`,
colour = .data$Type,
by = .data$Type)) +
ggplot2::geom_point() +
ggplot2::geom_line() +
ggplot2::scale_color_manual(values = c("forestgreen", "firebrick2")) +
ggplot2::scale_x_continuous(breaks = 1:x$J) +
ggplot2::theme_bw() +
ggplot2::theme(legend.position = "bottom",
legend.title = ggplot2::element_blank())
tau <- matrix(0, density, K)
if (all(delta_min < 0, delta_max > 0)) {
tau[, 1] <- c(seq(delta_min, -1e-6,
length.out = 0.5*density),
seq(1e-6, delta_max,
length.out = 0.5*density))
} else {
tau[, 1] <- seq(delta_min, delta_max, length.out = density)
}
for (k in 2:K) {
tau[, k] <- tau[, 1]
}
opchar_equal_og <- opchar_equal <- opchar_gs_norm(x, tau)$opchar
opchar_equal <-
tidyr::pivot_longer(opchar_equal, .data$`Pdis`:.data$MoSS,
names_to = "type", values_to = "P")
opchar_equal$type <- factor(opchar_equal$type,
c("Pdis", "Pcon", paste0("P", seq_K),
paste0("FWERI", seq_K),
paste0("FWERII", seq_K), "PHER",
"FDR", "pFDR", "FNDR", "Sens",
"Spec", "ESS", "SDSS", "MeSS",
"MoSS"))
labels_power <- numeric(K + 2)
labels_power[1:2] <- c(parse(text = "italic(P)[dis]"),
parse(text = "italic(P)[con]"))
for (i in 3:(K + 2)) {
labels_power[i] <- parse(text = paste("italic(P)[", i - 2, "]",
sep = ""))
}
colours_power <- ggthemes::ptol_pal()(K + 2)
plots$equal_power <- ggplot2::ggplot() +
ggplot2::geom_line(
data = dplyr::filter(opchar_equal, .data$type %in% c("Pdis", "Pcon",
paste0("P", seq_K))),
ggplot2::aes(.data$tau1, .data$P, col = .data$type)) +
ggplot2::scale_colour_manual(values = colours_power,
labels = labels_power) +
ggplot2::ylab("Probability") +
ggplot2::theme_bw() +
ggplot2::theme(legend.position = "bottom",
legend.title = ggplot2::element_blank(),
legend.spacing.x = grid::unit(0.2, "cm")) +
ggplot2::geom_hline(yintercept = alpha, linetype = 2) +
ggplot2::geom_hline(yintercept = 1 - beta, linetype = 2) +
ggplot2::geom_vline(xintercept = 0, linetype = 2) +
ggplot2::geom_vline(xintercept = delta1, linetype = 2)
if (K == 2) {
plots$equal_power <- plots$equal_power +
ggplot2::xlab(expression(paste(tau[1], " = ", tau[2], sep = "")))
} else if (K == 3) {
plots$equal_power <- plots$equal_power +
ggplot2::xlab(expression(paste(tau[1], " = ", tau[2], " = ", tau[3],
sep = "")))
} else {
plots$equal_power <- plots$equal_power +
ggplot2::xlab(bquote(paste(tau[1], " = ... = ", tau[.(K)], sep = "")))
}
if (print_plots) {
print(plots$equal_power)
}
labels_error <- numeric(2*K + 1)
for (i in seq_K) {
labels_error[i] <-
parse(text = paste("italic(FWER)[italic(I)][", i, "]", sep = ""))
labels_error[K + i] <-
parse(text = paste("italic(FWER)[italic(II)][", i, "]", sep = ""))
}
labels_error[2*K + 1] <- parse(text = "italic(PHER)")
colours_error <- ggthemes::ptol_pal()(2*K + 1)
plots$equal_error <- ggplot2::ggplot() +
ggplot2::geom_line(
data = dplyr::filter(opchar_equal,
(.data$type %in% c(paste0("FWERI", seq_K),
paste0("FWERII", seq_K), "PHER")) &
(.data$tau1 <= 0)),
ggplot2::aes(.data$tau1, .data$P, col = .data$type)) +
ggplot2::geom_line(
data = dplyr::filter(opchar_equal,
(.data$type %in% c(paste0("FWERI", seq_K),
paste0("FWERII", seq_K), "PHER")) &
(.data$tau1 > 0)),
ggplot2::aes(.data$tau1, .data$P, col = .data$type)) +
ggplot2::scale_colour_manual(values = colours_error,
labels = labels_error) +
ggplot2::ylab("Probability") +
ggplot2::theme_bw() +
ggplot2::theme(legend.position = "bottom",
legend.title = ggplot2::element_blank(),
legend.spacing.x = grid::unit(0.2, "cm")) +
ggplot2::geom_hline(yintercept = alpha, linetype = 2) +
ggplot2::geom_hline(yintercept = 1 - beta, linetype = 2) +
ggplot2::geom_vline(xintercept = 0, linetype = 2) +
ggplot2::geom_vline(xintercept = delta1, linetype = 2)
if (K == 2) {
plots$equal_error <- plots$equal_error +
ggplot2::xlab(expression(paste(tau[1], " = ", tau[2], sep = "")))
} else if (K == 3) {
plots$equal_error <- plots$equal_error +
ggplot2::xlab(expression(paste(tau[1], " = ", tau[2], " = ", tau[3],
sep = "")))
} else {
plots$equal_error <- plots$equal_error +
ggplot2::xlab(bquote(paste(tau[1], " = ... = ", tau[.(K)], sep = "")))
}
if (print_plots) {
print(plots$equal_error)
}
labels_other <- c(parse(text = "italic(FDR)"),
parse(text = "italic(pFDR)"),
parse(text = "italic(FNDR)"),
parse(text = "italic(Sensitivity)"),
parse(text = "italic(Specificity)"))
colours_other <- ggthemes::ptol_pal()(5)
plots$equal_other <- ggplot2::ggplot() +
ggplot2::geom_line(
data = dplyr::filter(opchar_equal,
(.data$type %in% c("FDR", "pFDR", "FNDR", "Sens",
"Spec")) & (.data$tau1 <= 0)),
ggplot2::aes(.data$tau1, .data$P, col = .data$type)) +
ggplot2::geom_line(
data = dplyr::filter(opchar_equal,
(.data$type %in% c("FDR", "pFDR", "FNDR", "Sens",
"Spec")) & (.data$tau1 > 0)),
ggplot2::aes(.data$tau1, .data$P, col = .data$type)) +
ggplot2::scale_colour_manual(values = colours_other,
labels = labels_other) +
ggplot2::ylab("Rate") +
ggplot2::theme_bw() +
ggplot2::theme(legend.position = "bottom",
legend.title = ggplot2::element_blank(),
legend.spacing.x = grid::unit(0.2, "cm")) +
ggplot2::geom_hline(yintercept = alpha, linetype = 2) +
ggplot2::geom_hline(yintercept = 1 - beta, linetype = 2) +
ggplot2::geom_vline(xintercept = 0, linetype = 2) +
ggplot2::geom_vline(xintercept = delta1, linetype = 2)
if (K == 2) {
plots$equal_other <- plots$equal_other +
ggplot2::xlab(expression(paste(tau[1], " = ", tau[2], sep = "")))
} else if (K == 3) {
plots$equal_other <- plots$equal_other +
ggplot2::xlab(expression(paste(tau[1], " = ", tau[2], " = ", tau[3],
sep = "")))
} else {
plots$equal_other <- plots$equal_other +
ggplot2::xlab(bquote(paste(tau[1], " = ... = ", tau[.(K)], sep = "")))
}
if (print_plots) {
print(plots$equal_other)
}
labels_ss <- c(parse(text = "italic(ESS)"),
parse(text = "italic(SDSS)"),
parse(text = "italic(MeSS)"),
parse(text = "italic(MoSS)"))
colours_ss <- ggthemes::ptol_pal()(4)
plots$equal_sample_size <- ggplot2::ggplot() +
ggplot2::geom_line(
data = dplyr::filter(opchar_equal,
(.data$type %in% c("ESS", "SDSS", "MeSS", "MoSS"))),
ggplot2::aes(.data$tau1, .data$P, col = .data$type)) +
ggplot2::scale_colour_manual(values = colours_ss,
labels = labels_ss) +
ggplot2::ylab("Value") +
ggplot2::theme_bw() +
ggplot2::theme(legend.position = "bottom",
legend.title = ggplot2::element_blank(),
legend.spacing.x = grid::unit(0.2, "cm")) +
ggplot2::geom_vline(xintercept = delta0, linetype = 2) +
ggplot2::geom_vline(xintercept = delta1, linetype = 2)
if (K == 2) {
plots$equal_sample_size <- plots$equal_sample_size +
ggplot2::xlab(expression(paste(tau[1], " = ", tau[2], sep = "")))
} else if (K == 3) {
plots$equal_sample_size <- plots$equal_sample_size +
ggplot2::xlab(expression(paste(tau[1], " = ", tau[2], " = ", tau[3],
sep = "")))
} else {
plots$equal_sample_size <- plots$equal_sample_size +
ggplot2::xlab(bquote(paste(tau[1], " = ... = ", tau[.(K)], sep = "")))
}
if (print_plots) {
print(plots$equal_sample_size)
}
if (all(summary, output)) {
message("..completed production of plots with equal treatment effects.")
message(" Beginning production of plots with shifted treatment effects..")
}
opchar_matrix <- NULL
tau_init <- matrix(seq(-delta1, 2*delta1,
length.out = density) - delta,
density, K)
tau <- tau_init
tau[, 1] <- tau[, 1] + delta
opchar_1 <- opchar_gs_norm(x, tau)$opchar
opchar_shifted_og <- opchar_shifted <- opchar_1
opchar_shifted <- tidyr::gather(opchar_shifted_og, "type",
"Value", .data$P1:.data$MoSS)
opchar_shifted$type <- factor(opchar_shifted$type,
c("P1", "ESS", "SDSS", "MeSS", "MoSS"))
plots$shifted_power <- ggplot2::ggplot() +
ggplot2::geom_line(data = dplyr::filter(opchar_shifted, .data$type == "P1"),
ggplot2::aes(.data$tau1, .data$Value)) +
ggplot2::xlab(bquote(paste(tau[1], " = ", tau[2], " + ",
.(delta), " = ... = ", tau[italic(K)],
" + ", .(delta), sep = ""))) +
ggplot2::ylab(expression(paste(italic(P), "(Reject ", italic(H)[1], " | ",
tau, ")", sep = ""))) +
ggplot2::theme_bw() +
ggplot2::geom_hline(yintercept = alpha, linetype = 2) +
ggplot2::geom_hline(yintercept = 1 - beta, linetype = 2) +
ggplot2::geom_vline(xintercept = delta1, linetype = 2)
if (print_plots) {
print(plots$shifted_power)
}
plots$shifted_sample_size <- ggplot2::ggplot() +
ggplot2::geom_line(
data = dplyr::filter(opchar_shifted, .data$type %in% c("ESS", "SDSS",
"MeSS", "MoSS")),
ggplot2::aes(.data$tau1, .data$Value, col = .data$type)) +
ggplot2::scale_colour_manual(values = colours_ss,
labels = labels_ss) +
ggplot2::xlab(bquote(paste(tau[1], " = ", tau[2], " + ",
.(delta), " = ... = ", tau[italic(K)],
" + ", .(delta), sep = ""))) +
ggplot2::ylab("Value") +
ggplot2::theme_bw() +
ggplot2::theme(legend.position = "bottom",
legend.title = ggplot2::element_blank(),
legend.spacing.x = grid::unit(0.2, "cm")) +
ggplot2::geom_vline(xintercept = delta1, linetype = 2)
if (print_plots) {
print(plots$shifted_sample_size)
}
if (all(summary, output)) {
message("..completed production of plots with shifted treatment effects.")
message(" Beginning production of sample size PMF plot..")
}
pmf_N <- opchar_gs_norm(x, rbind(rep(delta0, K), rep(delta1, K),
c(delta1, rep(delta0, K - 1))))$pmf_N
pmf_N <- dplyr::mutate(pmf_N,
Scenario = factor(rep(c("HG", "HA", "LFC1"),
each = nrow(pmf_N)/3),
levels = c("HG", "HA", "LFC1"),
labels = c("italic(H[G])",
"italic(H[A])",
"italic(LFC)[1]")))
pmf_N <- dplyr::mutate(pmf_N, n = factor(round(pmf_N$n, 1)))
plots$pmf_N <- ggplot2::ggplot(data = pmf_N,
mapping = ggplot2::aes(.data$n, .data$Prob)) +
ggplot2::geom_bar(stat = "identity", fill = "black", colour = "black") +
ggplot2::facet_wrap(.~Scenario, nrow = 1, ncol = 3,
labeller = ggplot2::label_parsed) +
ggplot2::xlab(expression(italic(n))) +
ggplot2::ylab(expression(paste(italic(P), "(", italic(N), " = ", italic(n),
")", sep = ""))) +
ggplot2::theme_bw()
if (print_plots) {
print(plots$pmf_N)
}
##### Outputting #############################################################
if (all(summary, output)) {
message("..completed production of sample size PMF plot.")
message(" Preparing for outputting..")
message("..outputting.")
}
if (output) {
list(density = density,
opchar = rbind(opchar_equal_og, opchar_shifted_og),
output = output,
plots = plots,
summary = summary,
x = x)
}
}