tests/testthat/test-plot_nma_data.R
In dmphillippo/multinma: Bayesian Network Meta-Analysis of Individual and Aggregate Data
skip_on_cran()
library(vdiffr)
library(ggplot2)
library(dplyr)
test_that("Atrial fibrillation", {
af_net <- set_agd_arm(atrial_fibrillation[atrial_fibrillation$studyc != "WASPO", ],
study = studyc,
trt = trtc,
r = r,
n = n,
trt_class = trt_class)
expect_doppelganger("Atrial fibrillation network",
plot(af_net, weight_nodes = TRUE, weight_edges = TRUE, show_trt_class = TRUE) +
ggplot2::theme(legend.position = "bottom", legend.box = "vertical")
)
expect_doppelganger("Atrial fibrillation network star",
plot(af_net, layout = "star")
)
})
test_that("BCG vaccine", {
bcg_net <- set_agd_arm(bcg_vaccine,
study = studyn,
trt = trtc,
r = r,
n = n,
trt_ref = "Unvaccinated")
expect_doppelganger("BCG vaccine network", plot(bcg_net))
})
test_that("Blocker", {
blocker_net <- set_agd_arm(blocker,
study = studyn,
trt = trtc,
r = r,
n = n,
trt_ref = "Control")
expect_doppelganger("Blocker network", plot(blocker_net))
})
test_that("Diabetes", {
db_net <- set_agd_arm(diabetes,
study = studyc,
trt = trtc,
r = r,
n = n)
expect_doppelganger("Diabetes network",
plot(db_net, weight_edges = TRUE, weight_nodes = TRUE) + ggplot2::theme(legend.box.margin = ggplot2::unit(c(0, 0, 0, 4), "lines"))
)
})
test_that("Dietary fat", {
diet_net <- set_agd_arm(dietary_fat,
study = studyc,
trt = trtc,
r = r,
E = E,
trt_ref = "Control",
sample_size = n)
expect_doppelganger("Dietary fat network", plot(diet_net))
})
test_that("HTA psoriasis", {
pso_net <- set_agd_arm(hta_psoriasis,
study = paste(studyc, year),
trt = trtc,
r = multi(r0 = sample_size - rowSums(cbind(PASI50, PASI75, PASI90), na.rm = TRUE),
PASI50, PASI75, PASI90,
inclusive = FALSE,
type = "ordered"))
expect_doppelganger("HTA psoriasis network",
plot(pso_net, weight_edges = TRUE, weight_nodes = TRUE) +
# Nudge the legend over
ggplot2::theme(legend.box.spacing = ggplot2::unit(0.75, "in"),
plot.margin = ggplot2::margin(0.1, 0, 0.1, 0.75, "in"))
)
})
test_that("Parkinsons", {
arm_net <- set_agd_arm(parkinsons,
study = studyn,
trt = trtn,
y = y,
se = se,
sample_size = n)
expect_doppelganger("Parkinsons arm network",
plot(arm_net, weight_edges = TRUE, weight_nodes = TRUE)
)
contr_net <- set_agd_contrast(parkinsons,
study = studyn,
trt = trtn,
y = diff,
se = se_diff,
sample_size = n)
expect_doppelganger("Parkinsons contrast network",
plot(contr_net, weight_edges = TRUE, weight_nodes = TRUE)
)
studies <- parkinsons$studyn
(parkinsons_arm <- parkinsons[studies %in% 1:3, ])
(parkinsons_contr <- parkinsons[studies %in% 4:7, ])
mix_arm_net <- set_agd_arm(parkinsons_arm,
study = studyn,
trt = trtn,
y = y,
se = se,
sample_size = n)
mix_contr_net <- set_agd_contrast(parkinsons_contr,
study = studyn,
trt = trtn,
y = diff,
se = se_diff,
sample_size = n)
mix_net <- combine_network(mix_arm_net, mix_contr_net)
expect_doppelganger("Parkinsons mixed network",
plot(mix_net, weight_edges = TRUE, weight_nodes = TRUE)
)
})
test_that("Plaque psoriasis", {
# IPD studies
pso_ipd <- plaque_psoriasis_ipd %>%
mutate(
# Variable transformations
bsa = bsa / 100,
weight = weight / 10,
durnpso = durnpso / 10,
prevsys = as.numeric(prevsys),
psa = as.numeric(psa),
# Treatment classes
trtclass = case_when(trtn == 1 ~ "Placebo",
trtn %in% c(2, 3, 5, 6) ~ "IL-17 blocker",
trtn == 4 ~ "TNFa blocker",
trtn == 7 ~ "IL-12/23 blocker"),
# Check complete cases for covariates of interest
is_complete = complete.cases(durnpso, prevsys, bsa, weight, psa)
) %>%
arrange(studyc, trtn) %>%
filter(is_complete)
# AgD studies
pso_agd <- plaque_psoriasis_agd %>%
mutate(
# Variable transformations
bsa_mean = bsa_mean / 100,
bsa_sd = bsa_sd / 100,
weight_mean = weight_mean / 10,
weight_sd = weight_sd / 10,
durnpso_mean = durnpso_mean / 10,
durnpso_sd = durnpso_sd / 10,
prevsys = prevsys / 100,
psa = psa / 100,
# Treatment classes
trtclass = case_when(trtn == 1 ~ "Placebo",
trtn %in% c(2, 3, 5, 6) ~ "IL-17 blocker",
trtn == 4 ~ "TNFa blocker",
trtn == 7 ~ "IL-12/23 blocker")
) %>%
arrange(studyc, trtn)
pso_net <- combine_network(
set_ipd(pso_ipd,
study = studyc,
trt = trtc,
r = multi(r0 = 1,
PASI75 = pasi75,
PASI90 = pasi90,
PASI100 = pasi100,
type = "ordered", inclusive = TRUE),
trt_class = trtclass),
set_agd_arm(pso_agd,
study = studyc,
trt = trtc,
r = multi(r0 = pasi75_n,
PASI75 = pasi75_r,
PASI90 = pasi90_r,
PASI100 = pasi100_r,
type = "ordered", inclusive = TRUE),
trt_class = trtclass)
)
class_pal <- c("#D95F02", "#7570B3", "#E7298A", "#E6AB02")
expect_doppelganger("Plaque psoriasis network",
plot(pso_net, weight_nodes = TRUE, weight_edges = TRUE, show_trt_class = TRUE, nudge = 0.1) +
ggraph::scale_edge_colour_manual("Data",
values = c(AgD = "#113259", IPD = "#55A480")) +
scale_fill_manual("Treatment class",
values = class_pal,
aesthetics = c("fill", "colour")) +
guides(edge_colour = guide_legend(override.aes = list(edge_width = 2)),
fill = guide_legend(override.aes = list(size = 2)))
)
})
test_that("Smoking", {
smknet <- set_agd_arm(smoking,
study = studyn,
trt = trtc,
r = r,
n = n,
trt_ref = "No intervention")
expect_doppelganger("Smoking network",
plot(smknet, weight_edges = TRUE, weight_nodes = TRUE) + ggplot2::theme(plot.margin = ggplot2::unit(c(1, 1, 1, 6), "lines"))
)
})
test_that("Statins", {
statin_net <- set_agd_arm(statins,
study = studyc,
trt = trtc,
r = r,
n = n,
trt_ref = "Placebo")
expect_doppelganger("Statins network",
plot(statin_net)
)
})
test_that("Thrombolytics", {
thrombo_net <- set_agd_arm(thrombolytics,
study = studyn,
trt = trtc,
r = r,
n = n)
expect_doppelganger("Thrombolytics network",
plot(thrombo_net, weight_edges = TRUE, weight_nodes = TRUE)
)
# Make trtf factor to order treatments in same way as Dias analysis
trts <- dplyr::distinct(thrombolytics, trtn, trtc)
trts <- dplyr::arrange(trts, trtn)
thrombolytics$trtf <- factor(thrombolytics$trtn, levels = trts$trtn, labels = trts$trtc)
thrombo_net2 <- set_agd_arm(thrombolytics,
study = studyn,
trt = trtf,
r = r,
n = n)
expect_doppelganger("Thrombolytics Dias network",
plot(thrombo_net2, weight_edges = TRUE, weight_nodes = TRUE)
)
})
test_that("Transfusion", {
tr_net <- set_agd_arm(transfusion,
study = studyc,
trt = trtc,
r = r,
n = n,
trt_ref = "Control")
expect_doppelganger("Transfusion network",
plot(tr_net)
)
})
test_that("NDMM", {
ndmm_ipd$trtclass <- case_match(ndmm_ipd$trtf,
"Pbo" ~ "Placebo",
c("Len", "Thal") ~ "Active")
ndmm_agd$trtclass <- case_match(ndmm_agd$trtf,
"Pbo" ~ "Placebo",
c("Len", "Thal") ~ "Active")
ndmm_net <- combine_network(
set_ipd(ndmm_ipd,
study = studyf,
trt = trtf,
trt_class = trtclass,
Surv = Surv(eventtime, status)),
set_agd_surv(ndmm_agd,
study = studyf,
trt = trtf,
trt_class = trtclass,
Surv = Surv(eventtime, status),
covariates = ndmm_agd_covs)
)
ndmm_net <- add_integration(ndmm_net,
age = distr(qgamma, mean = age_mean, sd = age_sd),
iss_stage3 = distr(qbern, iss_stage3),
response_cr_vgpr = distr(qbern, response_cr_vgpr),
male = distr(qbern, male))
expect_doppelganger("NDMM network",
plot(ndmm_net,
weight_nodes = TRUE,
weight_edges = TRUE,
# Nudge treatment labels away from nodes
nudge = 0.1,
# Manual layout
layout = data.frame(x = c(0, -1, 1),
y = c(-0.5, 0, 0))) +
guides(edge_colour = guide_legend(override.aes = list(edge_width = 2))) +
theme(legend.position = "bottom", legend.direction = "vertical")
)
})
test_that("Duplicated study arms plotted correctly", {
dat <- data.frame(study = "a",
trt = c(1, 2, 2, 2),
r = 1, n = 1)
net1 <- set_agd_arm(dat, study, trt, r = r, n = n)
expect_doppelganger("Duplicated AgD arms network", plot(net1))
net2 <- set_ipd(dat, study, trt, r = r)
expect_doppelganger("Duplicated IPD arms network", plot(net2))
dat_c <- data.frame(study = "a",
trt = c(1, 2, 2, 2),
y = c(NA, 1, 1, 1),
se = c(0.5, 1, 1, 1))
net3 <- set_agd_contrast(dat_c, study, trt, y = y, se = se)
expect_doppelganger("Duplicated AgD contrasts network", plot(net3))
})
dmphillippo/multinma documentation built on April 12, 2025, 11:41 a.m.
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skip_on_cran()
library(vdiffr)
library(ggplot2)
library(dplyr)
test_that("Atrial fibrillation", {
af_net <- set_agd_arm(atrial_fibrillation[atrial_fibrillation$studyc != "WASPO", ],
study = studyc,
trt = trtc,
r = r,
n = n,
trt_class = trt_class)
expect_doppelganger("Atrial fibrillation network",
plot(af_net, weight_nodes = TRUE, weight_edges = TRUE, show_trt_class = TRUE) +
ggplot2::theme(legend.position = "bottom", legend.box = "vertical")
)
expect_doppelganger("Atrial fibrillation network star",
plot(af_net, layout = "star")
)
})
test_that("BCG vaccine", {
bcg_net <- set_agd_arm(bcg_vaccine,
study = studyn,
trt = trtc,
r = r,
n = n,
trt_ref = "Unvaccinated")
expect_doppelganger("BCG vaccine network", plot(bcg_net))
})
test_that("Blocker", {
blocker_net <- set_agd_arm(blocker,
study = studyn,
trt = trtc,
r = r,
n = n,
trt_ref = "Control")
expect_doppelganger("Blocker network", plot(blocker_net))
})
test_that("Diabetes", {
db_net <- set_agd_arm(diabetes,
study = studyc,
trt = trtc,
r = r,
n = n)
expect_doppelganger("Diabetes network",
plot(db_net, weight_edges = TRUE, weight_nodes = TRUE) + ggplot2::theme(legend.box.margin = ggplot2::unit(c(0, 0, 0, 4), "lines"))
)
})
test_that("Dietary fat", {
diet_net <- set_agd_arm(dietary_fat,
study = studyc,
trt = trtc,
r = r,
E = E,
trt_ref = "Control",
sample_size = n)
expect_doppelganger("Dietary fat network", plot(diet_net))
})
test_that("HTA psoriasis", {
pso_net <- set_agd_arm(hta_psoriasis,
study = paste(studyc, year),
trt = trtc,
r = multi(r0 = sample_size - rowSums(cbind(PASI50, PASI75, PASI90), na.rm = TRUE),
PASI50, PASI75, PASI90,
inclusive = FALSE,
type = "ordered"))
expect_doppelganger("HTA psoriasis network",
plot(pso_net, weight_edges = TRUE, weight_nodes = TRUE) +
# Nudge the legend over
ggplot2::theme(legend.box.spacing = ggplot2::unit(0.75, "in"),
plot.margin = ggplot2::margin(0.1, 0, 0.1, 0.75, "in"))
)
})
test_that("Parkinsons", {
arm_net <- set_agd_arm(parkinsons,
study = studyn,
trt = trtn,
y = y,
se = se,
sample_size = n)
expect_doppelganger("Parkinsons arm network",
plot(arm_net, weight_edges = TRUE, weight_nodes = TRUE)
)
contr_net <- set_agd_contrast(parkinsons,
study = studyn,
trt = trtn,
y = diff,
se = se_diff,
sample_size = n)
expect_doppelganger("Parkinsons contrast network",
plot(contr_net, weight_edges = TRUE, weight_nodes = TRUE)
)
studies <- parkinsons$studyn
(parkinsons_arm <- parkinsons[studies %in% 1:3, ])
(parkinsons_contr <- parkinsons[studies %in% 4:7, ])
mix_arm_net <- set_agd_arm(parkinsons_arm,
study = studyn,
trt = trtn,
y = y,
se = se,
sample_size = n)
mix_contr_net <- set_agd_contrast(parkinsons_contr,
study = studyn,
trt = trtn,
y = diff,
se = se_diff,
sample_size = n)
mix_net <- combine_network(mix_arm_net, mix_contr_net)
expect_doppelganger("Parkinsons mixed network",
plot(mix_net, weight_edges = TRUE, weight_nodes = TRUE)
)
})
test_that("Plaque psoriasis", {
# IPD studies
pso_ipd <- plaque_psoriasis_ipd %>%
mutate(
# Variable transformations
bsa = bsa / 100,
weight = weight / 10,
durnpso = durnpso / 10,
prevsys = as.numeric(prevsys),
psa = as.numeric(psa),
# Treatment classes
trtclass = case_when(trtn == 1 ~ "Placebo",
trtn %in% c(2, 3, 5, 6) ~ "IL-17 blocker",
trtn == 4 ~ "TNFa blocker",
trtn == 7 ~ "IL-12/23 blocker"),
# Check complete cases for covariates of interest
is_complete = complete.cases(durnpso, prevsys, bsa, weight, psa)
) %>%
arrange(studyc, trtn) %>%
filter(is_complete)
# AgD studies
pso_agd <- plaque_psoriasis_agd %>%
mutate(
# Variable transformations
bsa_mean = bsa_mean / 100,
bsa_sd = bsa_sd / 100,
weight_mean = weight_mean / 10,
weight_sd = weight_sd / 10,
durnpso_mean = durnpso_mean / 10,
durnpso_sd = durnpso_sd / 10,
prevsys = prevsys / 100,
psa = psa / 100,
# Treatment classes
trtclass = case_when(trtn == 1 ~ "Placebo",
trtn %in% c(2, 3, 5, 6) ~ "IL-17 blocker",
trtn == 4 ~ "TNFa blocker",
trtn == 7 ~ "IL-12/23 blocker")
) %>%
arrange(studyc, trtn)
pso_net <- combine_network(
set_ipd(pso_ipd,
study = studyc,
trt = trtc,
r = multi(r0 = 1,
PASI75 = pasi75,
PASI90 = pasi90,
PASI100 = pasi100,
type = "ordered", inclusive = TRUE),
trt_class = trtclass),
set_agd_arm(pso_agd,
study = studyc,
trt = trtc,
r = multi(r0 = pasi75_n,
PASI75 = pasi75_r,
PASI90 = pasi90_r,
PASI100 = pasi100_r,
type = "ordered", inclusive = TRUE),
trt_class = trtclass)
)
class_pal <- c("#D95F02", "#7570B3", "#E7298A", "#E6AB02")
expect_doppelganger("Plaque psoriasis network",
plot(pso_net, weight_nodes = TRUE, weight_edges = TRUE, show_trt_class = TRUE, nudge = 0.1) +
ggraph::scale_edge_colour_manual("Data",
values = c(AgD = "#113259", IPD = "#55A480")) +
scale_fill_manual("Treatment class",
values = class_pal,
aesthetics = c("fill", "colour")) +
guides(edge_colour = guide_legend(override.aes = list(edge_width = 2)),
fill = guide_legend(override.aes = list(size = 2)))
)
})
test_that("Smoking", {
smknet <- set_agd_arm(smoking,
study = studyn,
trt = trtc,
r = r,
n = n,
trt_ref = "No intervention")
expect_doppelganger("Smoking network",
plot(smknet, weight_edges = TRUE, weight_nodes = TRUE) + ggplot2::theme(plot.margin = ggplot2::unit(c(1, 1, 1, 6), "lines"))
)
})
test_that("Statins", {
statin_net <- set_agd_arm(statins,
study = studyc,
trt = trtc,
r = r,
n = n,
trt_ref = "Placebo")
expect_doppelganger("Statins network",
plot(statin_net)
)
})
test_that("Thrombolytics", {
thrombo_net <- set_agd_arm(thrombolytics,
study = studyn,
trt = trtc,
r = r,
n = n)
expect_doppelganger("Thrombolytics network",
plot(thrombo_net, weight_edges = TRUE, weight_nodes = TRUE)
)
# Make trtf factor to order treatments in same way as Dias analysis
trts <- dplyr::distinct(thrombolytics, trtn, trtc)
trts <- dplyr::arrange(trts, trtn)
thrombolytics$trtf <- factor(thrombolytics$trtn, levels = trts$trtn, labels = trts$trtc)
thrombo_net2 <- set_agd_arm(thrombolytics,
study = studyn,
trt = trtf,
r = r,
n = n)
expect_doppelganger("Thrombolytics Dias network",
plot(thrombo_net2, weight_edges = TRUE, weight_nodes = TRUE)
)
})
test_that("Transfusion", {
tr_net <- set_agd_arm(transfusion,
study = studyc,
trt = trtc,
r = r,
n = n,
trt_ref = "Control")
expect_doppelganger("Transfusion network",
plot(tr_net)
)
})
test_that("NDMM", {
ndmm_ipd$trtclass <- case_match(ndmm_ipd$trtf,
"Pbo" ~ "Placebo",
c("Len", "Thal") ~ "Active")
ndmm_agd$trtclass <- case_match(ndmm_agd$trtf,
"Pbo" ~ "Placebo",
c("Len", "Thal") ~ "Active")
ndmm_net <- combine_network(
set_ipd(ndmm_ipd,
study = studyf,
trt = trtf,
trt_class = trtclass,
Surv = Surv(eventtime, status)),
set_agd_surv(ndmm_agd,
study = studyf,
trt = trtf,
trt_class = trtclass,
Surv = Surv(eventtime, status),
covariates = ndmm_agd_covs)
)
ndmm_net <- add_integration(ndmm_net,
age = distr(qgamma, mean = age_mean, sd = age_sd),
iss_stage3 = distr(qbern, iss_stage3),
response_cr_vgpr = distr(qbern, response_cr_vgpr),
male = distr(qbern, male))
expect_doppelganger("NDMM network",
plot(ndmm_net,
weight_nodes = TRUE,
weight_edges = TRUE,
# Nudge treatment labels away from nodes
nudge = 0.1,
# Manual layout
layout = data.frame(x = c(0, -1, 1),
y = c(-0.5, 0, 0))) +
guides(edge_colour = guide_legend(override.aes = list(edge_width = 2))) +
theme(legend.position = "bottom", legend.direction = "vertical")
)
})
test_that("Duplicated study arms plotted correctly", {
dat <- data.frame(study = "a",
trt = c(1, 2, 2, 2),
r = 1, n = 1)
net1 <- set_agd_arm(dat, study, trt, r = r, n = n)
expect_doppelganger("Duplicated AgD arms network", plot(net1))
net2 <- set_ipd(dat, study, trt, r = r)
expect_doppelganger("Duplicated IPD arms network", plot(net2))
dat_c <- data.frame(study = "a",
trt = c(1, 2, 2, 2),
y = c(NA, 1, 1, 1),
se = c(0.5, 1, 1, 1))
net3 <- set_agd_contrast(dat_c, study, trt, y = y, se = se)
expect_doppelganger("Duplicated AgD contrasts network", plot(net3))
})
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