-
Notifications
You must be signed in to change notification settings - Fork 5
/
plot.multiarm_des_dtl_bern.R
295 lines (286 loc) · 13.8 KB
/
plot.multiarm_des_dtl_bern.R
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
#' Plot operating characteristics of a multi-stage drop-the-losers multi-arm
#' clinical trial for a Bernoulli distributed primary outcome
#'
#' \code{plot.multiarm_des_dtl_bern()} produces power curve plots for a
#' specified multi-stage drop-the-losers multi-arm clinical trial design
#' assuming the primary outcome is Bernoulli distributed.
#'
#' @param x A \code{\link{list}} of class \code{"multiarm_des_dtl_bern"}, as
#' returned by \code{\link{build_dtl_bern}} or \code{\link{des_dtl_bern}} (i.e.,
#' a multi-stage drop-the-losers multi-arm clinical trial design for a Bernoulli
#' distributed outcome). Defaults to \code{des_dtl_bern()}.
#' @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$pi0 + 1e-6}.
#' @param delta_max A \code{\link{numeric}} specifying the chosen maximum
#' value for the treatment effects to include on the produced plots. Defaults to
#' \code{1 - x$pi0 - 1e-6}.
#' @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{$plots} containing the produced
#' plots.
#' \item Each of the input variables.
#' }
#' @examples
#' \dontrun{
#' # The design for the default parameters
#' des <- des_dtl_bern()
#' plot(des)
#' }
#' @seealso \code{\link{build_dtl_bern}}, \code{\link{des_dtl_bern}},
#' \code{\link{gui}}, \code{\link{opchar_dtl_bern}}, \code{\link{sim_dtl_bern}}.
#' @method plot multiarm_des_dtl_bern
#' @export
plot.multiarm_des_dtl_bern <- function(x = des_dtl_bern(),
delta_min = -x$pi0 + 1e-6,
delta_max = 1 - x$pi0 - 1e-6,
delta = x$delta1 - x$delta0,
density = 100, output = FALSE,
print_plots = TRUE, summary = FALSE,
...) {
##### Check input variables ##################################################
#check_multiarm_des_dtl_bern(x, name = "x")
check_real_range_strict(delta_min, "delta_min", c(-x$pi0, 1 - x$pi0), 1)
check_real_range_strict(delta_max, "delta_max", c(-x$pi0, 1 - x$pi0), 1)
if (delta_min >= delta_max) {
stop("delta_min must be strictly less than delta_max")
}
check_real_range_strict(delta, "delta", c(0, 1 - x$pi0), 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_dtl(x, delta_min, delta_max, delta, density,
# "bern")
message("")
}
##### Perform main computations ##############################################
if (all(summary, output)) {
message(" Beginning production of plots with equal treatment effects..")
}
K <- x$Kv[1]
alpha <- x$alpha
beta <- x$beta
pi0 <- x$pi0
delta0 <- x$delta0
delta1 <- x$delta1
seq_K <- 1:K
plots <- list()
pi <- cbind(rep(pi0, density), matrix(0, density, K))
if (all(delta_min < 0, delta_max > 0)) {
pi[, 2] <- pi0 + c(seq(delta_min, -1e-6,
length.out = 0.5*density),
seq(1e-6, delta_max,
length.out = 0.5*density))
} else {
pi[, 2] <- pi0 + seq(delta_min, delta_max,
length.out = density)
}
for (k in 3:(K + 1)) {
pi[, k] <- pi[, 2]
}
opchar_equal_og <- opchar_equal <- opchar_dtl_bern(x, pi)$opchar
opchar_equal <-
tidyr::pivot_longer(opchar_equal, .data$`Pdis`:.data$Spec,
names_to = "type", values_to = "P")
opchar_equal$type <- factor(opchar_equal$type,
c("Pdis", "Pcon", paste0("P", seq_K),
paste0("FWERI", seq_K),
paste0("FWERII", seq_K), "PHER",
"FDR", "pFDR", "FNDR", "Sens",
"Spec"))
labels_power <- numeric(K + 2)
labels_power[1:2] <- c(parse(text = "italic(P)[dis]"),
parse(text = "italic(P)[con]"))
for (i in 3:(K + 2)) {
labels_power[i] <- parse(text = paste("italic(P)[", i - 2, "]",
sep = ""))
}
colours_power <- ggthemes::ptol_pal()(K + 2)
plots$equal_power <- ggplot2::ggplot() +
ggplot2::geom_line(
data = dplyr::filter(opchar_equal, .data$type %in% c("Pdis", "Pcon",
paste0("P", seq_K))),
ggplot2::aes(.data$pi1, .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 = pi0, linetype = 2) +
ggplot2::geom_vline(xintercept = pi0 + delta1, linetype = 2)
if (K == 2) {
plots$equal_power <- plots$equal_power +
ggplot2::xlab(bquote(paste(pi[0], " = ", .(pi0), ", ", pi[1], " = ",
pi[2], sep = "")))
} else if (K == 3) {
plots$equal_power <- plots$equal_power +
ggplot2::xlab(bquote(paste(pi[0], " = ", .(pi0), ", ", pi[1], " = ",
pi[2], " = ", pi[3], sep = "")))
} else {
plots$equal_power <- plots$equal_power +
ggplot2::xlab(bquote(paste(pi[0], " = ", .(pi0), ", ", pi[1], " = ... = ",
pi[.(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$type %in% c(paste0("FWERI", seq_K),
paste0("FWERII", seq_K), "PHER")) &
(.data$pi1 <= .data$pi0)),
ggplot2::aes(.data$pi1, .data$P, col = .data$type)) +
ggplot2::geom_line(
data = dplyr::filter(opchar_equal,
(.data$type %in% c(paste0("FWERI", seq_K),
paste0("FWERII", seq_K), "PHER")) &
(.data$pi1 > .data$pi0)),
ggplot2::aes(.data$pi1, .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 = pi0, linetype = 2) +
ggplot2::geom_vline(xintercept = pi0 + delta1, linetype = 2)
if (K == 2) {
plots$equal_error <- plots$equal_error +
ggplot2::xlab(bquote(paste(pi[0], " = ", .(pi0), ", ", pi[1], " = ",
pi[2], sep = "")))
} else if (K == 3) {
plots$equal_error <- plots$equal_error +
ggplot2::xlab(bquote(paste(pi[0], " = ", .(pi0), ", ", pi[1], " = ",
pi[2], " = ", pi[3], sep = "")))
} else {
plots$equal_error <- plots$equal_error +
ggplot2::xlab(bquote(paste(pi[0], " = ", .(pi0), ", ", pi[1], " = ... = ",
pi[.(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$type %in% c("FDR", "pFDR", "FNDR", "Sens",
"Spec")) & (.data$pi1 <= .data$pi0)),
ggplot2::aes(.data$pi1, .data$P, col = .data$type)) +
ggplot2::geom_line(
data = dplyr::filter(opchar_equal,
(.data$type %in% c("FDR", "pFDR", "FNDR", "Sens",
"Spec")) & (.data$pi1 > .data$pi0)),
ggplot2::aes(.data$pi1, .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 = pi0, linetype = 2) +
ggplot2::geom_vline(xintercept = pi0 + delta1, linetype = 2)
if (K == 2) {
plots$equal_other <- plots$equal_other +
ggplot2::xlab(bquote(paste(pi[0], " = ", .(pi0), ", ", pi[1], " = ",
pi[2], sep = "")))
} else if (K == 3) {
plots$equal_other <- plots$equal_other +
ggplot2::xlab(bquote(paste(pi[0], " = ", .(pi0), ", ", pi[1], " = ",
pi[2], " = ", pi[3], sep = "")))
} else {
plots$equal_other <- plots$equal_other +
ggplot2::xlab(bquote(paste(pi[0], " = ", .(pi0), ", ", pi[1], " = ... = ",
pi[.(K)], sep = "")))
}
if (print_plots) {
print(plots$equal_other)
}
if (all(summary, output)) {
message("..completed production of plots with equal treatment effects.")
message(" Beginning production of plots with shifted treatment effects..")
}
pi <- cbind(rep(pi0, density), matrix(0, density, K))
pi[, 2] <- pi0 + seq(delta_min, delta_max,
length.out = density)
for (l in seq_K[-1]) {
pi[, l + 1] <- pi[, 2] - delta
}
pi <- pi[as.logical(apply(pi >= 0, 1, prod)), ]
opchar_1 <- opchar_dtl_bern(x, pi)$opchar
opchar_shifted_og <- opchar_shifted <- opchar_1
plots$shifted_power <- ggplot2::ggplot() +
ggplot2::geom_line(data = opchar_shifted,
ggplot2::aes(.data$pi1, .data$P1)) +
ggplot2::xlab(bquote(paste(pi[0], " = ", .(pi0), ", ", pi[1], " = ",
pi[2], " + ", .(delta), " = ... = ",
pi[italic(K)], " + ", .(delta), sep = ""))) +
ggplot2::ylab(expression(paste(italic(P), "(Reject ", italic(H)[1], " | ",
pi, ")", sep = ""))) +
ggplot2::theme_bw() +
ggplot2::geom_hline(yintercept = alpha, linetype = 2) +
ggplot2::geom_hline(yintercept = 1 - beta, linetype = 2) +
ggplot2::geom_vline(xintercept = pi0 + delta1, linetype = 2)
if (print_plots) {
print(plots$shifted_power)
}
if (all(summary, output)) {
message("..completed production of plots with shifted treatment effects.")
}
##### Outputting #############################################################
if (all(summary, output)) {
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)
}
}