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### DSA Plot Generator v1.0
# For more information, check the publication by Vreman et al
# "The application and implications of novel deterministic sensitivity analysis methods"
# Copyright (C) 2019, Rick Vreman
# This program is free software: you can redistribute it and/or modify it under
# the terms of the GNU General Public License as published by the
# Free Software Foundation, either version 3 of the License,
# or (at your option) any later version.
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
# See the MIT License for more details
library('ggplot2')
library('ggpubr')
library('stringr')
library('forcats')
library('directlabels')
library('ggrepel')
### --------------------- Data to be used as input. ---------------------- ###
# Your raw data should have the following characteristics
# - Saved as a .csv file
# - Eight colums, in this order:
# - 1) 'Scenario': Number of scenario (starting with 1, 2, 3, ...)
# - 2) 'Parameter': Short parameter name
# - 3) 'ScenarioNumb': Scenario per parameter
# (this goes from 1 to 21 for each parameter, 1 being the lowest)
# - 4) 'Bound': whether this scenario is 'lower', 'upper' or 'base case'
# - 5) 'ICER': value of the ICER for each scenario
# - 6) 'Labels': at scenario number 1, 11 (base case) and 21 for each
# parameter, the applicable values for the parameter
# - 7) 'TypeRange': the range that is applied
# (e.g. 95% CI, -50% to +50%, or others)
# - 8) 'FullName': the full parameter name to be displayed in the graph
# Then, prepare to load your data:
# - set the filename of your raw datafile, do not forget the .csv
# (e.g. "c://mydocuments/DSA/datainput.csv") )
# - set whether your data has a header (ie, column names, this should be TRUE)
# - set the type of decimal seperator (, or . within " ")
# - set the column separator used for your .csv file (within " ").
# You can see the seperator when opening your csv file with, for example, Excel.
### ---------------------------- Load data -------------------------------- ###
datSDSA <- read.csv2(file="C:/InputDSA.csv",header=TRUE,sep = ";",dec = ",")
### ------------------------End of Loading data --------------------------- ###
### ----------------------------------------------------------------------- ###
### -----------------Getting data ready for the SDSA plot------------------ ###
dat <- datSDSA
# Set ICER values as numerical
dat$ICER <- as.numeric(as.character(dat$ICER))
# You may get a warning that NAs are introduced, this happens if ICERs are
# missing (e.g. not calculable ICERs)
# Set NA ICERs at 0 (this default is decribed in the publication)
dat$ICER[is.na(dat$ICER)] <- 0
# Set the base case ICER
ICER.basecase <- unique(dat$ICER[dat$Bound == 'base case'])
ICER.BCround <- round(ICER.basecase,0)
# Calculate ICERs relative to the base case instead of absolute
dat$relICER <- dat$ICER - ICER.basecase
# Replace base case bound with NA value (necessary for plot generation)
dat$Bound[dat$Bound == "base case"] <- NA
# Create the names to be used in the plot,
# including the parameter name + the description of the range
dat$Parameter2 <- paste(dat$FullName, "(", dat$TypeRange, ")")
dat$Parameter2 <- gsub('\\(', '\n(', dat$Parameter2)
## Construction of the order and the labels of the plot
# Get a vector of the short parameter names
parameters <- as.character(unlist(unique(dat$Parameter)))
# Calculate the absolute range of each parameter (for plot order)
absrange <- as.numeric(c(rep(NA, length(parameters))))
for (i in 1:length(parameters)) {
absrange[i] <- abs(max(dat$relICER[dat$Parameter == parameters[i]]) -
min(dat$relICER[dat$Parameter == parameters[i]]))
}
dat$CatOrder <- rep(absrange, each = max(dat$ScenarioNumb))
orderdat <- order(dat$CatOrder)
# Create labels in order
LabelsOrdered <- dat$Labels[orderdat]
LabelsOrdered <- as.numeric(as.character(LabelsOrdered))
# Generate a vector for the positions of the labels of the plot
labelpos <- as.numeric()
for (i in 0:(length(unique(dat$Parameter)) - 1)) {
labelpos <- c(labelpos,i + 0.65)
labelpos <- c(labelpos,i + 1)
labelpos <- c(labelpos,i + 1.35)
}
dat$xCoords[!is.na(dat$Labels)] <- labelpos
# Combine the label text with the position of the labels into a data frame
labelsdat <- cbind(dat$xCoords, LabelsOrdered)
### ----------------End of getting data ready for the plot----------------- ###
### ----------------------------------------------------------------------- ###
### ----------------------------------------------------------------------- ###
### --------------------------Generating the plot-------------------------- ###
# Set limits of the graph. With your own data you will probably have to try
# a few times before getting it right
# Get minimum
min(dat$relICER)
# Get maximum
max(dat$relICER)
# Set limits (round down from min and up from max)
xaxismin <- -50000
xaxismax <- 68000
limits <- c(xaxismin, xaxismax)
# Set where labels are placed,
# the minimum should be slightly higher than the min limit
breaks <- c(-20000, 60000)
# Set how often labels are placed
breaks.by <- 20000
# Set the values displayed in the labels
xlabels <- seq(breaks[1], breaks[2], breaks.by)
breaksset <- seq(-20000 - ICER.basecase, 60000 - ICER.basecase, breaks.by)
datnumbers <- dat$ScenarioNumb + 10
dat$dodge <- paste(dat$Bound,datnumbers)
colorsdodge <- c(rep("#F8766D",10), "#000000", rep("#00BFC4",10))
# sDSA plot
ggSDSA <- ggplot(data = dat, aes(x = Parameter2, y = relICER, fill = dodge)) +
geom_bar(stat = "identity", position = "dodge",
aes(x = fct_reorder(dat$Parameter2, dat$CatOrder, .desc = FALSE))) +
theme(axis.ticks.y = element_blank()) +
theme(panel.grid = element_blank()) +
theme(panel.grid.major.x = element_line(colour = "grey80")) +
theme(panel.grid.minor.x = element_line(colour = "grey80")) +
scale_y_continuous(name = paste("Incremental Cost-Effectiveness Ratio (base case \u20ac",
ICER.BCround,
"/QALY)"),
breaks = breaksset,
limits = limits, labels = xlabels) +
scale_fill_manual(breaks = c("lower 11","upper 31"),
labels=c("Lower range", "Upper range"),
values = colorsdodge) +
scale_x_discrete(name = "Parameter",
breaks = dat$Parameter2,
labels = dat$Parameter2) +
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.text = element_text(size = 14)) +
theme(axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16),
axis.title.y = element_blank()) +
geom_vline(xintercept = seq(1, length(dat[, 1]), 1) + 0.5, color = "grey74") +
geom_hline(yintercept = -ICER.basecase, color = "grey50") +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
annotate("text", x = labelsdat[, 1], y = xaxismin, label = labelsdat[, 2],
colour = "grey50", size = 5, hjust = 0) +
coord_flip()
# cDSA plot
dat2 <- dat[!is.na(dat$Labels), ]
ggCDSA <- ggplot(data= dat2, aes(x = FullName, y = relICER, fill = Bound)) +
geom_bar(stat = "identity",
aes(x = fct_reorder(dat2$FullName, dat2$CatOrder, .desc = FALSE))) +
theme(axis.ticks.y = element_blank()) +
theme(panel.grid = element_blank()) +
theme(panel.grid.major.x = element_line(colour = "grey80")) +
theme(panel.grid.minor.x = element_line(colour = "grey80")) +
scale_y_continuous(name = paste("Incremental Cost-Effectiveness Ratio (base case \u20ac",
ICER.BCround,
"/QALY)"),
breaks = breaksset,
limits = limits, labels = xlabels) +
scale_fill_manual(breaks = c("lower", "upper"),
labels=c("Lower range", "Upper range"),
values = c("#F8766D", "#00BFC4")) +
scale_x_discrete(name = "Parameter",
breaks = dat2$FullName,
labels = dat2$FullName) +
theme(axis.title.y = element_blank()) +
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.text = element_text(size = 14)) +
theme(axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16)) +
geom_vline(xintercept = seq(1, length(dat[, 1]), 1) + 0.5, color = "grey74") +
geom_hline(yintercept = -ICER.basecase, color = "grey50") +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
coord_flip()
# Combine both plots in 1 figure
final.figure <- ggarrange(ggCDSA, ggSDSA, heights = 1,
ncol = 2, nrow = 1, align = "hv")
final.figure
### ----------------------------------------------------------------------- ###
### -------------------- Options for Saving your plot --------------------- ###
## This figure number matches that of the publication
png(filename="C:/Figure1.png", units="cm", width=45, height=30, res=1200)
plot(final.figure)
dev.off()
### -------------------- Figure 3 and 4 for SDSA --------------------- ###
# Third figure showing dominated sections
dat$domin <- NA
dat <- within(dat, {
f <- IncrC <= 0 & IncrQ > 0
domin[f] <- 'Dominates'
g <- IncrC > 0 & IncrQ <= 0
domin[g] <- 'Dominated'
})
alphas <- c(rep(0.8, 52), rep(0.1, 100), rep(1, 100))
dat$Alphas <- NA
dat <- within(dat, {
f <- IncrC <= 0 & IncrQ > 0
Alphas[f] <- 0.45
g <- IncrC > 0 & IncrQ <= 0
domin[g] <- 0.8
h <- IncrC > 0 & IncrQ >= 0
Alphas[h] <- 0.5
i <- IncrC <= 0 & IncrQ < 0
Alphas[i] <- 0.5
})
ggSDSAdom <- ggplot(data = dat, aes(x = Parameter2, y = relICER, fill = dodge)) +
geom_bar(stat = "identity", position = "dodge",
aes(x = fct_reorder(dat$Parameter2, dat$CatOrder, .desc = FALSE), alpha = dat$Alphas)) +
theme(axis.ticks.y = element_blank()) +
theme(panel.grid = element_blank()) +
theme(panel.grid.major.x = element_line(colour = "grey80")) +
theme(panel.grid.minor.x = element_line(colour = "grey80")) +
scale_y_continuous(name = paste("Incremental Cost-Effectiveness Ratio (base case \u20ac",
ICER.BCround,
"/QALY)"),
breaks = breaksset,
limits = limits, labels = xlabels) +
scale_fill_manual(breaks = c("lower 11","upper 31"),
labels=c("Lower range", "Upper range"),
values = colorsdodge) +
scale_x_discrete(name = "Parameter",
breaks = dat$Parameter2,
labels = dat$Parameter2) +
scale_alpha(range = c(0.6, 1.0), guide = 'none') +
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.text = element_text(size = 14)) +
theme(axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16),
axis.title.y = element_blank()) +
geom_vline(xintercept = seq(1, length(dat[, 1]), 1) + 0.5, color = "grey74") +
geom_hline(yintercept = -ICER.basecase, color = "grey50") +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
annotate("text", x = labelsdat[, 1], y = xaxismin, label = labelsdat[, 2],
colour = "grey50", size = 5, hjust = 0) +
coord_flip()
# Fourth figure with INMB
dat$INMB <- (dat$IncrQ * 20000) - dat$IncrC
SDSA.BC.INMB <- (dat$IncrQ[11] * 20000) - dat$IncrC[11]
dat$relINMB <- dat$INMB - SDSA.BC.INMB
min(dat$INMB)
max(dat$INMB)
xaxismininmb <- -10000
xaxismaxinmb <- 10000
limitsinmb <- c(xaxismininmb, xaxismaxinmb)
# Set where labels are placed,
# the minimum should be slightly higher than the min limit
breaksinmb <- c(-10000, 10000)
# Set how often labels are placed
breaks.byinmb <- 5000
# Set the values displayed in the labels
xlabelsinmb <- seq(breaksinmb[1], breaksinmb[2], breaks.byinmb)
breakssetinmb <- seq(-10000 - SDSA.BC.INMB, 10000 - SDSA.BC.INMB, breaks.byinmb)
ggSDSAINMB <- ggplot(data = dat, aes(x = Parameter2, y = relINMB, fill = dodge)) +
geom_bar(stat = "identity", position = "dodge",
aes(x = fct_reorder(dat$Parameter2, dat$CatOrder, .desc = FALSE), alpha = dat$Alphas)) +
theme(axis.ticks.y = element_blank()) +
theme(panel.grid = element_blank()) +
theme(panel.grid.major.x = element_line(colour = "grey80")) +
theme(panel.grid.minor.x = element_line(colour = "grey80")) +
scale_y_continuous(name = paste("Incremental Net Monetary Benefit (base case \u20ac",
SDSA.BC.INMB,
")"),
breaks = breakssetinmb,
limits = limitsinmb, labels = xlabelsinmb) +
scale_fill_manual(breaks = c("lower 11","upper 31"),
labels=c("Lower range", "Upper range"),
values = colorsdodge) +
scale_x_discrete(name = "Parameter",
breaks = dat$Parameter2,
labels = dat$Parameter2) +
scale_alpha(range = c(0.6, 1.0), guide = 'none') +
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.text = element_text(size = 14)) +
theme(axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16),
axis.title.y = element_blank()) +
geom_vline(xintercept = seq(1, length(dat[, 1]), 1) + 0.5, color = "grey74") +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
geom_hline(yintercept = -SDSA.BC.INMB, color = "grey50") +
annotate("text", x = labelsdat[, 1], y = xaxismininmb, label = labelsdat[, 2],
colour = "grey50", size = 5, hjust = 0) +
coord_flip()
final.figure2 <- ggarrange(ggSDSAdom, ggSDSAINMB, heights = 1,
ncol = 2, nrow = 1, align = "hv")
final.figure2
png(filename="C:/Figure2.png", units="cm", width=45, height=30, res=1200)
plot(final.figure2)
dev.off()
### ---------------------------End of SDSA--------------------------------- ###
### ----------------------------------------------------------------------- ###
### ----------------------------------------------------------------------- ###
### --------------------------- Start of DDSA ----------------------------- ###
datDDSA <- read.csv2(file="C:/RinputDDSA.csv",header=TRUE,sep = ";",dec = ",")
datdec <- datDDSA
datdec$ICER <- as.numeric(as.character(datdec$ICER))
datdec$ICER[is.na(datdec$ICER)] <- 0
ICER.basecase.dec <- unique(datdec$ICER[datdec$Bound == 'base case'])
ICER.BCrounddec <- round(ICER.basecase.dec,0)
datdec$relICER <- datdec$ICER - ICER.basecase.dec
datdec$Bound[datdec$Bound == "base case"] <- NA
datdec$Parameter2 <- paste(datdec$FullName, "(", datdec$TypeRange, ")")
datdec$Parameter2 <- gsub('\\(', '\n(', datdec$Parameter2)
parametersdec <- as.character(unlist(unique(datdec$Parameter)))
absrangedec <- as.numeric(c(rep(NA, length(parametersdec))))
for (i in 1:length(parametersdec)) {
absrangedec[i] <- abs(max(datdec$relICER[datdec$Parameter == parametersdec[i]]) -
min(datdec$relICER[datdec$Parameter == parametersdec[i]]))
}
datdec$CatOrder <- rep(absrangedec, each = max(datdec$ScenarioNumb))
orderdatdec <- order(datdec$CatOrder)
LabelsOrdereddec <- datdec$Labels[orderdatdec]
LabelsOrdereddec <- as.numeric(as.character(LabelsOrdereddec))
# Generate a vector for the positions of the labels of the plot
labelposdec <- as.numeric()
for (i in 0:(length(unique(datdec$Parameter)) - 1)) {
labelposdec <- c(labelposdec,i + 0.65)
labelposdec <- c(labelposdec,i + 1)
labelposdec <- c(labelposdec,i + 1.35)
}
datdec$xCoords[!is.na(datdec$Labels)] <- labelposdec
datdec$domin <- NA
datdec <- within(datdec, {
f <- IncrC <= 0 & IncrQ > 0
domin[f] <- 'Dominates'
g <- IncrC > 0 & IncrQ <= 0
domin[g] <- 'Dominated'
})
alphasdec <- c(rep(0.8, 52), rep(0.1, 100), rep(1, 100))
datdec$Alphas <- NA
datdec <- within(datdec, {
f <- IncrC <= 0 & IncrQ > 0
Alphas[f] <- 0.45
g <- IncrC > 0 & IncrQ <= 0
domin[g] <- 0.8
h <- IncrC > 0 & IncrQ >= 0
Alphas[h] <- 0.5
i <- IncrC <= 0 & IncrQ < 0
Alphas[i] <- 0.5
})
# Combine the label text with the position of the labels into a data frame
labelsdatdec <- cbind(datdec$xCoords, LabelsOrdereddec)
# Set plot ranges
min(datdec$relICER)
max(datdec$relICER)
xaxismindec <- -50000
xaxismaxdec <- 68000
limitsdec <- c(xaxismindec, xaxismaxdec)
breaksdec <- c(-20000, 60000)
breaks.bydec <- 20000
xlabelsdec <- seq(breaksdec[1], breaksdec[2], breaks.bydec)
breakssetdec <- seq(-20000 - ICER.basecase.dec, 60000 - ICER.basecase.dec, breaks.by)
# Make the dodging of the bars nicer
datdecnumbers <- datdec$ScenarioNumb + 10
datdec$dodge <- paste(datdec$Bound,datnumbers)
deccolorsdodge <- c(rep("#F8766D",10), "#000000", rep("#00BFC4",10))
# DDSA plot
ggDDSA <- ggplot(data = datdec, aes(x = Parameter2, y = relICER, fill = dodge)) +
geom_bar(stat = "identity", position = "dodge",
aes(x = fct_reorder(datdec$Parameter2, dat$CatOrder, .desc = FALSE), alpha = datdec$Alphas)) +
theme(axis.ticks.y = element_blank()) +
theme(panel.grid = element_blank()) +
theme(panel.grid.major.x = element_line(colour = "grey80")) +
theme(panel.grid.minor.x = element_line(colour = "grey80")) +
scale_y_continuous(name = paste("Incremental Cost-Effectiveness Ratio (base case \u20ac",
ICER.BCrounddec,
"/QALY)"),
breaks = breakssetdec,
limits = limitsdec, labels = xlabelsdec) +
scale_fill_manual(breaks = c("lower 11","upper 31"),
labels=c("Lower range", "Upper range"),
values = deccolorsdodge) +
scale_x_discrete(name = "Parameter",
breaks = datdec$Parameter2,
labels = datdec$Parameter2) +
scale_alpha(range = c(0.6, 1.0), guide = 'none') +
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.text = element_text(size = 14)) +
theme(axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16),
axis.title.y = element_blank()) +
geom_vline(xintercept = seq(1, length(datdec[, 1]), 1) + 0.5, color = "grey74") +
geom_hline(yintercept = -ICER.basecase.dec, color = "grey50") +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
annotate("text", x = labelsdat[, 1], y = xaxismindec, label = labelsdat[, 2],
colour = "grey50", size = 5, hjust = 0) +
coord_flip()
# Combine both plots in 1 figure
final.figure2.1 <- ggarrange(ggSDSAdom, ggDDSA, heights = 1,
ncol = 2, nrow = 1, align = "hv")
final.figure2.1
png(filename="C:/Figure3.png", units="cm", width=45, height=30, res=1200)
plot(final.figure2.1)
dev.off()
###------------------------Figure 4 of DDSA---------------------------###
# Figure 4a: DDSA with INMB
datdec$INMB <- (datdec$IncrQ * 20000) - datdec$IncrC
DDSA.BC.INMB <- (datdec$IncrQ[11] * 20000) - datdec$IncrC[11]
DDSA.BC.INMB.round <- round(DDSA.BC.INMB,0)
datdec$relINMB <- datdec$INMB - DDSA.BC.INMB
min(datdec$INMB)
max(datdec$INMB)
xaxismininmbdec <- -10000
xaxismaxinmbdec <- 10000
limitsinmbdec <- c(xaxismininmbdec, xaxismaxinmbdec)
breaksinmbdec <- c(-10000, 10000)
breaks.byinmbdec <- 5000
xlabelsinmbdec <- seq(breaksinmbdec[1], breaksinmbdec[2], breaks.byinmbdec)
breakssetinmbdec <- seq(-10000 - DDSA.BC.INMB, 10000 - DDSA.BC.INMB, breaks.byinmbdec)
ggDDSAINMB <- ggplot(data = datdec, aes(x = Parameter2, y = relINMB, fill = dodge)) +
geom_bar(stat = "identity", position = "dodge",
aes(x = fct_reorder(datdec$Parameter2, dat$CatOrder, .desc = FALSE), alpha = datdec$Alphas)) +
theme(axis.ticks.y = element_blank()) +
theme(panel.grid = element_blank()) +
theme(panel.grid.major.x = element_line(colour = "grey80")) +
theme(panel.grid.minor.x = element_line(colour = "grey80")) +
scale_y_continuous(name = paste("Incremental Net Monetary Benefit (base case \u20ac",
DDSA.BC.INMB.round,
")"),
breaks = breakssetinmbdec,
limits = limitsinmbdec, labels = xlabelsinmbdec) +
scale_fill_manual(breaks = c("lower 11","upper 31"),
labels=c("Lower range", "Upper range"),
values = deccolorsdodge) +
scale_x_discrete(name = "Parameter",
breaks = datdec$Parameter2,
labels = datdec$Parameter2) +
scale_alpha(range = c(0.6, 1.0), guide = 'none') +
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.text = element_text(size = 14)) +
theme(axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16),
axis.title.y = element_blank()) +
geom_vline(xintercept = seq(1, length(datdec[, 1]), 1) + 0.5, color = "grey74") +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
geom_hline(yintercept = -DDSA.BC.INMB, color = "grey50") +
annotate("text", x = labelsdat[, 1], y = xaxismininmbdec, label = labelsdat[, 2],
colour = "grey50", size = 5, hjust = 0) +
coord_flip()
## Figure 4b: DDSA INMB in line chart
DDSAdeciles <- c(seq(0.025, 0.475, 0.05),0.5,seq(0.525, 0.975, 0.05))
DDSAdecilesfull <- rep(decilesprob, 12)
datdec$Deciles <- DDSAdecilesfull
linetype <- c(rep(c(1,1,1,1),3)) # can be 0 to 6, with 0 being blank, 1 solid, 2 dashed .... etc
markertype <- seq(0, 11, 1)
verticallines <- c(0.025, 0.125,0.25, 0.75,0.875,0.975)
xscalebreaks <- c(0.025, 0.125, 0.250, 0.500, 0.750, 0.875, 0.975)
xscalelimits <- c(0,1)
xscalelabels <- c("2.5", "12.5", "25.0", "50.0", "75.0", "87.5", "97.5")
## Line plot DDSA with INMB
ggDDSAINMBline <- ggplot(data = datdec, aes(x = Deciles, y = relINMB, group = Parameter2)) +
geom_line(aes(color=Parameter2, linetype=Parameter2)) +
geom_point(aes(color=Parameter2, shape = Parameter2)) +
scale_linetype_manual(values = linetype) +
scale_shape_manual(values = markertype) +
scale_y_continuous(name = paste("Incremental Net Monetary Benefit (base case \u20ac",
DDSA.BC.INMB.round,
")"),
breaks = breakssetinmbdec,
limits = limitsinmbdec, labels = xlabelsinmbdec) +
#xlab("Percentile of parameter distribution") +
scale_x_continuous(name = "Percentile of parameter distribution",
breaks = xscalebreaks,
labels = xscalelabels,
limits = xscalelimits) +
geom_vline(xintercept = verticallines, linetype="dashed",color = "grey80", size = 1) +
theme(panel.background = element_rect(fill="white", colour = "black"),
panel.grid.major.y = element_line(colour="grey80"),
panel.grid.major.x = element_blank(),
legend.title = element_blank(),
legend.position = "right",
legend.text = element_text(size = 14),
axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16)
#,axis.ticks.y = element_line(colour="grey80")
) +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
geom_hline(yintercept = -DDSA.BC.INMB, color = "grey50")
ggDDSAINMBline
# Combine in one plot
final.figure2.2 <- ggarrange(ggDDSAINMB, ggDDSAINMBline, heights = 1,
ncol = 2, nrow = 1, align = "hv", widths = c(1, 1.2))
final.figure2.2
png(filename="C:/Figure4.png", units="cm", width=49.5, height=30, res=1200)
plot(final.figure2.2)
dev.off()
### ---------------------------End of DDSA--------------------------------- ###
### ----------------------------------------------------------------------- ###
### ----------------------------------------------------------------------- ###
### --------------------------- Start of PDSA ----------------------------- ###
## The data is loaded in 5 separate CSVs (since Excel has a maximum number of rows and we needed 2.5 million)
datPDSA1 <- read.csv2(file="C:/RinputProb v3.1.csv",header=TRUE,sep = ";",dec = ",")
datPDSA1 <- datPDSA1[2:504001,]
datPDSA2 <- read.csv2(file="C:/RinputProb v3.2.csv",header=TRUE,sep = ";",dec = ",")
datPDSA2 <- datPDSA2[2:504001,]
datPDSA3 <- read.csv2(file="C:/RinputProb v3.3.csv",header=TRUE,sep = ";",dec = ",")
datPDSA3 <- datPDSA3[2:504001,]
datPDSA4 <- read.csv2(file="C:/RinputProb v3.4.csv",header=TRUE,sep = ";",dec = ",")
datPDSA4 <- datPDSA4[2:504001,]
datPDSA5 <- read.csv2(file="C:/RinputProb v3.5.csv",header=TRUE,sep = ";",dec = ",")
datPDSA5 <- datPDSA5[2:504001,]
datPDSA <- rbind(datPDSA1, datPDSA2, datPDSA3, datPDSA4, datPDSA5)
vectexcl <- c(which(datPDSA$IncrC>200000))
datexclPDSA <- datPDSA
datexclPDSA[vectexcl,] <- NA
datexclPDSA$parval <- rep(c(rep(1:252, each = 2000)),5)
datexclPDSA$INMB <- (datexclPDSA$IncrQ * 20000) - datexclPDSA$IncrC
datexclPDSAs <- aggregate(datexclPDSA[,1:21],by=list(datexclPDSA$parval), mean, na.rm=TRUE)
datpromeans <- apply(datexclPDSA,MARGIN = 2,mean, na.rm = TRUE)
datprosds <- apply(datexclPDSA,MARGIN = 2,sd, na.rm = TRUE)
ICER.BC.PDSA <- as.numeric(datpromeans[17] / datpromeans[18])
ICER.BC.PDSA.round <- round(ICER.BC.PDSA,0)
#datprob <- datPDSA
#datprob$parval <- rep(c(rep(1:252, each = 2000)),5)
#datprob$INMB <- (datprob$IncrQ * 20000) - datprob$IncrC
#names(datprob)[names(datprob) == "ICER"] <- "ICERprob"
#datstats <- aggregate(datprob[,1:21],by=list(datprob$parval), mean)
datpro <- cbind(datexclPDSAs, datdec[,1:4],datdec[,6:8],datdec[,12:14])
names(datpro)[names(datpro) == "ICER"] <- "ICERprob"
# Checking the range of ICERs
mean(datpro$IncrQ[1:84])
median(datpro$IncrQ[1:84])
incrqvar <- datpro$IncrQ[1:84] - 0.35994
max(incrqvar)
min(incrqvar)
mean(incrqvar)
quantile(incrqvar, 0.75)
incrqvarind <- max(incrqvar)
# ICER calculations
datpro$ICERfromCQ <- datpro$IncrC / datpro$IncrQ
datpro$relICERfromCQ <- datpro$ICERfromCQ - ICER.BC.PDSA
proICERbc <- datpro$ICERfromCQ[datpro$ScenarioNumb==11]
proICERsBC <- rep(proICERbc, each = 21)
datpro$relICERfromCQpar <- datpro$ICERfromCQ - proICERsBC
# Dominated info
datpro$domin <- NA
datpro <- within(datpro, {
f <- IncrC <= 0 & IncrQ > 0
domin[f] <- 'Dominates'
g <- IncrC > 0 & IncrQ <= 0
domin[g] <- 'Dominated'
})
alphaspro <- c(rep(0.8, 52), rep(0.1, 100), rep(1, 100))
datpro$Alphas <- NA
datpro <- within(datpro, {
f <- IncrC <= 0 & IncrQ > 0
Alphas[f] <- 0.45
g <- IncrC > 0 & IncrQ <= 0
domin[g] <- 0.8
h <- IncrC > 0 & IncrQ >= 0
Alphas[h] <- 0.5
i <- IncrC <= 0 & IncrQ < 0
Alphas[i] <- 0.5
})
# Ordering if necessary
parameterspro <- as.character(unlist(unique(datpro$Parameter)))
absrangepro <- as.numeric(c(rep(NA, length(parameterspro))))
for (i in 1:length(parameterspro)) {
absrangepro[i] <- abs(max(datpro$relICERfromCQpar[datpro$Parameter == parameterspro[i]]) -
min(datpro$relICERfromCQpar[datpro$Parameter == parameterspro[i]]))
}
datpro$CatOrder <- rep(absrangepro, each = max(datpro$ScenarioNumb))
orderdatpro <- order(datpro$CatOrder)
# Ordering INMB if necessary
parametersproinmb <- as.character(unlist(unique(datpro$Parameter)))
absrangeproinmb <- as.numeric(c(rep(NA, length(parametersproinmb))))
for (i in 1:length(parametersproinmb)) {
absrangeproinmb[i] <- abs(max(datpro$relINMBpar[datpro$Parameter == parametersproinmb[i]]) -
min(datpro$relINMBpar[datpro$Parameter == parametersproinmb[i]]))
}
datpro$CatOrderInmb <- rep(absrangeproinmb, each = max(datpro$ScenarioNumb))
orderdatproinmb <- order(datpro$CatOrderInmb)
# Set limits and breaks
min(datpro$relICERfromCQ)
max(datpro$relICERfromCQ)
xaxisminpro <- -50000
xaxismaxpro <- 98000
limitspro <- c(xaxisminpro, xaxismaxpro)
breakspro <- c(-20000, 80000)
breaks.bypro <- 20000
xlabelspro <- seq(breakspro[1], breakspro[2], breaks.bypro)
breakssetpro <- seq(-20000 - ICER.BC.PDSA, 80000 - ICER.BC.PDSA, breaks.bypro)
# Nicer dodge
datpronumbers <- datpro$ScenarioNumb + 10
datpro$dodge <- paste(datpro$Bound,datnumbers)
procolorsdodge <- c(rep("#F8766D",10), "#000000", rep("#00BFC4",10))
# First plot (ICER unstacked)
ggPDSA <- ggplot(data = datpro, aes(x = Parameter2, y = relICERfromCQpar, fill = dodge)) +
geom_bar(stat = "identity", position = "dodge",
aes(x = fct_reorder(datpro$Parameter2, dat$CatOrder, .desc = FALSE), alpha = datpro$Alphas)) +
theme(axis.ticks.y = element_blank()) +
theme(panel.grid = element_blank()) +
theme(panel.grid.major.x = element_line(colour = "grey80")) +
theme(panel.grid.minor.x = element_line(colour = "grey80")) +
scale_y_continuous(name = paste("Incremental Cost-Effectiveness Ratio (prob. means)"
#\u20ac",
#ICER.BC.PDSA.round,
#"/QALY)"
),
breaks = breakssetpro,
limits = limitspro, labels = xlabelspro) +
scale_fill_manual(breaks = c("lower 11","upper 31"),
labels=c("Lower range", "Upper range"),
values = procolorsdodge) +
scale_x_discrete(name = "Parameter",
breaks = datpro$Parameter2,
labels = datpro$Parameter2) +
scale_alpha(range = c(0.6, 1.0), guide = 'none') +
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.text = element_text(size = 14)) +
theme(axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16),
axis.title.y = element_blank()) +
geom_vline(xintercept = seq(1, length(datpro[, 1]), 1) + 0.5, color = "grey74") +
geom_hline(yintercept = -ICER.BC.PDSA, color = "grey50") +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
annotate("text", x = labelsdat[, 1], y = xaxismindec, label = labelsdat[, 2],
colour = "grey50", size = 5, hjust = 0) +
coord_flip()
ggPDSA
# Combine both plots in 1 figure
final.figure3.1 <- ggarrange(ggDDSA, ggPDSA, heights = 1,
ncol = 2, nrow = 1, align = "hv")
final.figure3.1
png(filename="C:/Figure7.png", units="cm", width=45, height=30, res=1200)
plot(final.figure3.1)
dev.off()
##------------------- PDSA third and fourth figure--------------------###
# Figure 3: PDSA with INMB
datpro$INMBfromMeanCQ <- (datpro$IncrQ * 20000) - datpro$IncrC
PDSA.BC.INMB <- as.numeric((datpromeans[18] * 20000) - datpromeans[17])
PDSA.BC.INMB.round <- as.numeric(round(PDSA.BC.INMB,0))
datpro$relINMB <- datpro$INMBfromMeanCQ - PDSA.BC.INMB
proINMBbc <- datpro$INMBfromMeanCQ[datpro$ScenarioNumb==11]
proINMBsBC <- rep(proINMBbc, each = 21)
datpro$relINMBpar <- datpro$INMBfromMeanCQ - proINMBsBC
min(datpro$INMB)
max(datpro$INMB)
xaxismininmbpro <- -10000
xaxismaxinmbpro <- 10000
limitsinmbpro <- c(xaxismininmbpro, xaxismaxinmbpro)
breaksinmbpro <- c(-10000, 10000)
breaks.byinmbpro <- 5000
xlabelsinmbpro <- seq(breaksinmbpro[1], breaksinmbpro[2], breaks.byinmbpro)
breakssetinmbpro <- seq(-10000 - PDSA.BC.INMB, 10000 - PDSA.BC.INMB, breaks.byinmbpro)
ggPDSAINMB <- ggplot(data = datpro, aes(x = Parameter2, y = relINMBpar, fill = dodge)) +
geom_bar(stat = "identity", position = "dodge",
aes(x = fct_reorder(datpro$Parameter2, dat$CatOrder, .desc = FALSE), alpha = datpro$Alphas)) +
theme(axis.ticks.y = element_blank()) +
theme(panel.grid = element_blank()) +
theme(panel.grid.major.x = element_line(colour = "grey80")) +
theme(panel.grid.minor.x = element_line(colour = "grey80")) +
scale_y_continuous(name = paste("Incremental Net Monetary Benefit (prob. means)"
#\u20ac",
#PDSA.BC.INMB.round,
#")"
),
breaks = breakssetinmbpro,
limits = limitsinmbpro, labels = xlabelsinmbpro) +
scale_fill_manual(breaks = c("lower 11","upper 31"),
labels=c("Lower range", "Upper range"),
values = procolorsdodge) +
scale_x_discrete(name = "Parameter",
breaks = datpro$Parameter2,
labels = datpro$Parameter2) +
scale_alpha(range = c(0.6, 1.0), guide = 'none') +
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.text = element_text(size = 14)) +
theme(axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16),
axis.title.y = element_blank()) +
geom_vline(xintercept = seq(1, length(datpro[, 1]), 1) + 0.5, color = "grey74") +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
geom_hline(yintercept = -PDSA.BC.INMB, color = "grey50") +
annotate("text", x = labelsdat[, 1], y = xaxismininmbpro, label = labelsdat[, 2],
colour = "grey50", size = 5, hjust = 0) +
coord_flip()
ggPDSAINMB
## Fourth figure: PDSA INMB in line chart
PDSAdeciles <- c(seq(0.025, 0.475, 0.05),0.5,seq(0.525, 0.975, 0.05))
PDSAdecilesfull <- rep(PDSAdeciles, 12)
datpro$Deciles <- DDSAdecilesfull
linetype <- c(rep(c(1,1,1,1),3)) # can be 0 to 6, with 0 being blank, 1 solid, 2 dashed .... etc
markertype <- seq(0, 11, 1)
verticallines <- c(0.025, 0.125,0.25, 0.75,0.875,0.975)
xscalebreaks <- c(0.025, 0.125, 0.250, 0.500, 0.750, 0.875, 0.975)
xscalelimits <- c(0,1)
xscalelabels <- c("2.5", "12.5", "25.0", "50.0", "75.0", "87.5", "97.5")
## Line plot PDSA with INMB
ggPDSAINMBline <- ggplot(data = datpro, aes(x = Deciles, y = relINMBpar, group = Parameter2)) +
geom_line(aes(color=Parameter2, linetype=Parameter2)) +
geom_point(aes(color=Parameter2, shape = Parameter2)) +
scale_linetype_manual(values = linetype) +
scale_shape_manual(values = markertype) +
scale_y_continuous(name = paste("Incremental Net Monetary Benefit (prob. means)"
#\u20ac",
#PDSA.BC.INMB.round,
#")"
),
breaks = breakssetinmbpro,
limits = limitsinmbpro, labels = xlabelsinmbpro) +
#xlab("Percentile of parameter distribution") +
scale_x_continuous(name = "Percentile of parameter distribution",
breaks = xscalebreaks,
labels = xscalelabels,
limits = xscalelimits
#,expand = expand_scale(mult = c(.4, .4))
) +
geom_vline(xintercept = verticallines, linetype="dashed",color = "grey80", size = 1) +
theme(panel.background = element_rect(fill="white", colour = "black"),
panel.grid.major.y = element_line(colour="grey80"),
panel.grid.major.x = element_blank(),
legend.title = element_blank(),
legend.position = "right",
legend.text = element_text(size = 14),
axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
#axis.ticks.y = element_line(colour="grey80")
) +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
geom_hline(yintercept = -PDSA.BC.INMB, color = "grey50")
final.figure3.2 <- ggarrange(ggPDSAINMB, ggPDSAINMBline, heights = 1,
ncol = 2, nrow = 1, align = "hv", widths = c(1, 1.2))
final.figure3.2
png(filename="C:/Figure8.png", units="cm", width=49.5, height=30, res=1200)
plot(final.figure3.2)
dev.off()
### PDSA INMB with categories reordered
LabelsOrderedproinmb <- datpro$Labels[orderdatproinmb]
LabelsOrderedproinmbs <- as.numeric(as.character(LabelsOrderedproinmb))
labelsdatproinmb <- cbind(datpro$xCoords, LabelsOrderedproinmbs)
ggPDSAINMBorder <- ggplot(data = datpro, aes(x = Parameter2, y = relINMBpar, fill = dodge)) +
geom_bar(stat = "identity", position = "dodge",
aes(x = fct_reorder(datpro$Parameter2, datpro$CatOrderInmb, .desc = FALSE), alpha = datpro$Alphas)) +
theme(axis.ticks.y = element_blank()) +
theme(panel.grid = element_blank()) +
theme(panel.grid.major.x = element_line(colour = "grey80")) +
theme(panel.grid.minor.x = element_line(colour = "grey80")) +
scale_y_continuous(name = paste("Incremental Net Monetary Benefit (prob. means)"
#\u20ac",
#PDSA.BC.INMB.round,
#")"
),
breaks = breakssetinmbpro,
limits = limitsinmbpro, labels = xlabelsinmbpro) +
scale_fill_manual(breaks = c("lower 11","upper 31"),
labels=c("Lower range", "Upper range"),
values = procolorsdodge) +
scale_x_discrete(name = "Parameter",
breaks = datpro$Parameter2,
labels = datpro$Parameter2) +
scale_alpha(range = c(0.6, 1.0), guide = 'none') +
theme(legend.title = element_blank(),
legend.position = "bottom",
legend.text = element_text(size = 14)) +
theme(axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16),
axis.title.y = element_blank()) +
geom_vline(xintercept = seq(1, length(datpro[, 1]), 1) + 0.5, color = "grey74") +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
geom_hline(yintercept = -PDSA.BC.INMB, color = "grey50") +
annotate("text", x = labelsdatproinmb[, 1], y = xaxismininmbpro, label = labelsdatproinmb[, 2],
colour = "grey50", size = 5, hjust = 0) +
coord_flip()
ggPDSAINMBorder
final.figure4 <- ggarrange(ggCDSA, ggPDSAINMBorder, heights = 1,
ncol = 2, nrow = 1, align = "hv", widths = c(1, 1))
final.figure4
png(filename="C:/Figure9.png", units="cm", width=45, height=30, res=1200)
plot(final.figure4)
dev.off()
### ---------------------------End of PDSA--------------------------------- ###
### ----------------------------------------------------------------------- ###
### With direct labels for line plots
ggPDSAINMBlinelabels <- ggplot(data = datpro, aes(x = Deciles, y = relINMBpar, group = Parameter2)) +
geom_line(aes(color=Parameter2, linetype=Parameter2)) +
geom_point(aes(color=Parameter2, shape = Parameter2)) +
scale_linetype_manual(values = linetype) +
scale_shape_manual(values = markertype) +
scale_y_continuous(name = paste("Incremental Net Monetary Benefit (prob. means)"
#\u20ac",
#PDSA.BC.INMB.round,
#")"
),
breaks = breakssetinmbpro,
limits = limitsinmbpro, labels = xlabelsinmbpro) +
#xlab("Percentile of parameter distribution") +
scale_x_continuous(name = "Percentile of parameter distribution",
breaks = xscalebreaks,
labels = xscalelabels,
limits = xscalelimits
,expand = expand_scale(mult = c(0, .5))
) +
geom_vline(xintercept = verticallines, linetype="dashed",color = "grey80", size = 1) +
theme(panel.background = element_rect(fill="white", colour = "black"),
panel.grid.major.y = element_line(colour="grey80"),
panel.grid.major.x = element_blank(),
legend.title = element_blank(),
legend.position = "right",
legend.text = element_text(size = 14),
axis.text.x = element_text(size = 13),
axis.text.y = element_text(size = 14),
axis.title.x = element_text(size = 16),
axis.title.y = element_text(size = 16),
#axis.ticks.y = element_line(colour="grey80")
) +
geom_hline(yintercept = 0, color = "grey50", linetype = "dashed") +
geom_hline(yintercept = -PDSA.BC.INMB, color = "grey50")
ggPDSAINMBll <- ggPDSAINMBlinelabels +
geom_dl(aes(label = datpro$FullName, colour=datpro$Parameter2),
method = list("last.bumpup"),
cex = 1.5,
hjust = 1)
#+ theme(plot.margin = unit(c(1,12,1,1), "lines")) #+
#theme(plot.margin = unit(c(0,4,0,0), "cm"))
final.figure5 <- ggarrange(ggPDSAINMB, ggPDSAINMBll, heights = 1,
ncol = 2, nrow = 1, align = "hv", widths = c(1, 1.2))
final.figure5
png(filename="C:/Figure8.png", units="cm", width=49.5, height=30, res=1200)
plot(final.figure5)
dev.off()
ggPDSAINMBline +
geom_dl(aes(label = datpro$FullName, colour=datpro$Parameter2),
method = list("first.bumpup"),
cex = 1.5,
hjust = 1) #+
ggPDSAINMBlinelabels +
geom_text(
aes(label = datpro$FullName, colour=datpro$Parameter2, x = Inf, y = values), hjust = -.2) +
scale_x_continuous(expand = c(0, 0)) +
scale_colour_discrete(guide = 'none') +
theme_bw() +
theme(plot.margin = unit(c(1,3,1,1), "lines"))
## Figure 3
uniformdist <- c(600,
663.1578947,
726.3157895,
789.4736842,
852.6315789,
915.7894737,