#
# VANTDET:
# output table of key cost-effectiveness statistics for rule-out test
# output_ruleout.R
# N Green
# HIV?
source(file = "scripts/define-decision-tree.R")
dat <- list()
dat[['transcript']] <- dectree(
data = data,
name.newtest = "transcript",
costDistns = costs,
performance = list(performance$transcript_ruleout),
time_res = list(time_res$transcript),
drug = drug,
QALYloss = QALYloss,
terminal_cost = terminal_cost_ruleout,
terminal_health = terminal_health_ruleout)
dat[['proteomic_SELDI']] <- dectree(
data = data,
name.newtest = "proteomic_SELDI",
costDistns = costs,
performance = list(performance$proteomic_SELDI_ruleout),
time_res = list(time_res$proteomic_SELDI),
drug = drug,
QALYloss = QALYloss,
terminal_cost = terminal_cost_ruleout,
terminal_health = terminal_health_ruleout)
# dat[['microscopy']] <- dectree(
# data = data,
# name.newtest = "microscopy",
# costDistns = costs,
# performance = list(performance$microscopy),
# time_res = list(time_res$microscopy),
# drug = drug,
# QALYloss = QALYloss,
# terminal_cost = terminal_cost_ruleout,
# terminal_health = terminal_health_ruleout)
e_df <- do.call(cbind, purrr::map(dat, 'e'))
c_df <- do.call(cbind, purrr::map(dat, 'c'))
evens <- seq(from = 2, to = 2*length(dat), 2)
odds <- evens - 1
QALYgain <- as.matrix(data.frame(0, e_df[ ,odds] - e_df[ ,evens]))
cost_incur <- as.matrix(data.frame(0, c_df[ ,evens] - c_df[ ,odds]))
res_bcea <- bcea(e = -QALYgain,
c = -cost_incur,
interventions = c("status-quo", names(dat)))
##########
# output #
##########
contour2(res_bcea, graph = "ggplot2")
my_contour2(res_bcea, graph = "ggplot2", CONTOUR_PC = '5%') +
coord_cartesian(xlim = c(-0.002, 0.002)) +
theme(legend.position = "none")
my_contour2_facet(dat)
cost_effectiveness_table(dat)
result_tab <- cost_effectiveness_table(res_bcea)
write.csv(x = result_tab,
file = "output/ICERtable_ruleout.csv")
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