# NOT RUN {
## Smoking cessation
# Set up network of smoking cessation data
head(smoking)
smk_net <- set_agd_arm(smoking,
study = studyn,
trt = trtc,
r = r,
n = n,
trt_ref = "No intervention")
# Print details
smk_net
# }
# NOT RUN {
# Fitting a random effects model
smk_fit_RE <- nma(smk_net,
trt_effects = "random",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 100),
prior_het = normal(scale = 5))
smk_fit_RE
# }
# NOT RUN {
# }
# NOT RUN {
# Predicted log odds of success in each study in the network
predict(smk_fit_RE)
# Predicted probabilities of success in each study in the network
(smk_pred_RE <- predict(smk_fit_RE, type = "response"))
plot(smk_pred_RE, ref_line = c(0, 1))
# Predicted probabilities in a population with a baseline log odds of
# response on No Intervantion given a Normal distribution with mean -2
# and SD 0.15
predict(smk_fit_RE, baseline = distr(qnorm, mean = -2, sd = 0.15))
# }
# NOT RUN {
## Plaque psoriasis ML-NMR
# Set up plaque psoriasis network combining IPD and AgD
library(dplyr)
pso_ipd <- filter(plaque_psoriasis_ipd,
studyc %in% c("UNCOVER-1", "UNCOVER-2", "UNCOVER-3"))
pso_agd <- filter(plaque_psoriasis_agd,
studyc == "FIXTURE")
head(pso_ipd)
head(pso_agd)
pso_ipd <- pso_ipd %>%
mutate(# Variable transformations
bsa = bsa / 100,
prevsys = as.numeric(prevsys),
psa = as.numeric(psa),
weight = weight / 10,
durnpso = durnpso / 10,
# Treatment classes
trtclass = case_when(trtn == 1 ~ "Placebo",
trtn %in% c(2, 3, 5, 6) ~ "IL blocker",
trtn == 4 ~ "TNFa blocker"),
# Check complete cases for covariates of interest
complete = complete.cases(durnpso, prevsys, bsa, weight, psa)
)
pso_agd <- pso_agd %>%
mutate(
# Variable transformations
bsa_mean = bsa_mean / 100,
bsa_sd = bsa_sd / 100,
prevsys = prevsys / 100,
psa = psa / 100,
weight_mean = weight_mean / 10,
weight_sd = weight_sd / 10,
durnpso_mean = durnpso_mean / 10,
durnpso_sd = durnpso_sd / 10,
# Treatment classes
trtclass = case_when(trtn == 1 ~ "Placebo",
trtn %in% c(2, 3, 5, 6) ~ "IL blocker",
trtn == 4 ~ "TNFa blocker")
)
# Exclude small number of individuals with missing covariates
pso_ipd <- filter(pso_ipd, complete)
pso_net <- combine_network(
set_ipd(pso_ipd,
study = studyc,
trt = trtc,
r = pasi75,
trt_class = trtclass),
set_agd_arm(pso_agd,
study = studyc,
trt = trtc,
r = pasi75_r,
n = pasi75_n,
trt_class = trtclass)
)
# Print network details
pso_net
# Add integration points to the network
pso_net <- add_integration(pso_net,
durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd),
prevsys = distr(qbern, prob = prevsys),
bsa = distr(qlogitnorm, mean = bsa_mean, sd = bsa_sd),
weight = distr(qgamma, mean = weight_mean, sd = weight_sd),
psa = distr(qbern, prob = psa),
n_int = 1000)
# }
# NOT RUN {
# Fitting a ML-NMR model.
# Specify a regression model to include effect modifier interactions for five
# covariates, along with main (prognostic) effects. We use a probit link and
# specify that the two-parameter Binomial approximation for the aggregate-level
# likelihood should be used. We set treatment-covariate interactions to be equal
# within each class. We narrow the possible range for random initial values with
# init_r = 0.1, since probit models in particular are often hard to initialise.
# Using the QR decomposition greatly improves sampling efficiency here, as is
# often the case for regression models.
pso_fit <- nma(pso_net,
trt_effects = "fixed",
link = "probit",
likelihood = "bernoulli2",
regression = ~(durnpso + prevsys + bsa + weight + psa)*.trt,
class_interactions = "common",
prior_intercept = normal(scale = 10),
prior_trt = normal(scale = 10),
prior_reg = normal(scale = 10),
init_r = 0.1,
QR = TRUE)
# }
# NOT RUN {
# }
# NOT RUN {
# Predicted probabilities of response in each study in the network
(pso_pred <- predict(pso_fit, type = "response"))
plot(pso_pred, ref_line = c(0, 1))
# Predicted probabilites of response in a new target population, with means
# and SDs or proportions given by
new_agd_int <- data.frame(
bsa_mean = 0.6,
bsa_sd = 0.3,
prevsys = 0.1,
psa = 0.2,
weight_mean = 10,
weight_sd = 1,
durnpso_mean = 3,
durnpso_sd = 1
)
# We need to add integration points to this data frame of new data
# We use the weighted mean correlation matrix computed from the IPD studies
new_agd_int <- add_integration(new_agd_int,
durnpso = distr(qgamma, mean = durnpso_mean, sd = durnpso_sd),
prevsys = distr(qbern, prob = prevsys),
bsa = distr(qlogitnorm, mean = bsa_mean, sd = bsa_sd),
weight = distr(qgamma, mean = weight_mean, sd = weight_sd),
psa = distr(qbern, prob = psa),
cor = pso_net$int_cor,
n_int = 1000)
# Predicted probabilities of achieving PASI 75 in this target population, given
# a Normal(-1.75, 0.08^2) distribution on the baseline probit-probability of
# response on Placebo (at the reference levels of the covariates), are given by
(pso_pred_new <- predict(pso_fit,
type = "response",
newdata = new_agd_int,
baseline = distr(qnorm, -1.75, 0.08)))
plot(pso_pred_new, ref_line = c(0, 1))
# }
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