# 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 fixed effect model
smk_fit_FE <- nma(smk_net,
trt_effects = "fixed",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 100))
smk_fit_FE
# }
# NOT RUN {
# }
# 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 {
# Compare DIC of FE and RE models
(smk_dic_FE <- dic(smk_fit_FE))
(smk_dic_RE <- dic(smk_fit_RE)) # substantially better fit
# Plot residual deviance contributions under RE model
plot(smk_dic_RE)
# Check for inconsistency using UME model
# }
# NOT RUN {
# Fitting an unrelated mean effects (inconsistency) model
smk_fit_RE_UME <- nma(smk_net,
consistency = "ume",
trt_effects = "random",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 100),
prior_het = normal(scale = 5))
smk_fit_RE_UME
# }
# NOT RUN {
# }
# NOT RUN {
# Compare DIC
smk_dic_RE
(smk_dic_RE_UME <- dic(smk_fit_RE_UME)) # no difference in fit
# Compare residual deviance contributions
plot(smk_dic_RE, smk_dic_RE_UME, show_uncertainty = FALSE)
# }
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