Calling example("example_smk_ume") will run an unrelated mean
effects (inconsistency) NMA model with the smoking cessation data, using the
code in the Examples section below. The resulting stan_nma object
smk_fit_RE_UME will then be available in the global environment.
Examples
# Set up network of smoking cessation data
head(smoking)
#> studyn trtn trtc r n
#> 1 1 1 No intervention 9 140
#> 2 1 3 Individual counselling 23 140
#> 3 1 4 Group counselling 10 138
#> 4 2 2 Self-help 11 78
#> 5 2 3 Individual counselling 12 85
#> 6 2 4 Group counselling 29 170
smk_net <- set_agd_arm(smoking,
study = studyn,
trt = trtc,
r = r,
n = n,
trt_ref = "No intervention")
# Print details
smk_net
#> A network with 24 AgD studies (arm-based).
#>
#> ------------------------------------------------------- AgD studies (arm-based) ----
#> Study Treatment arms
#> 1 3: No intervention | Group counselling | Individual counselling
#> 2 3: Group counselling | Individual counselling | Self-help
#> 3 2: No intervention | Individual counselling
#> 4 2: No intervention | Individual counselling
#> 5 2: No intervention | Individual counselling
#> 6 2: No intervention | Individual counselling
#> 7 2: No intervention | Individual counselling
#> 8 2: No intervention | Individual counselling
#> 9 2: No intervention | Individual counselling
#> 10 2: No intervention | Self-help
#> ... plus 14 more studies
#>
#> Outcome type: count
#> ------------------------------------------------------------------------------------
#> Total number of treatments: 4
#> Total number of studies: 24
#> Reference treatment is: No intervention
#> Network is connected
# \donttest{
# 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
#> A random effects NMA with a binomial likelihood (logit link).
#> An inconsistency model ('ume') was fitted.
#> Inference for Stan model: binomial_1par.
#> 4 chains, each with iter=2000; warmup=1000; thin=1;
#> post-warmup draws per chain=1000, total post-warmup draws=4000.
#>
#> mean se_mean sd 2.5%
#> d[Group counselling vs. No intervention] 1.13 0.02 0.81 -0.40
#> d[Individual counselling vs. No intervention] 0.89 0.01 0.27 0.37
#> d[Self-help vs. No intervention] 0.36 0.01 0.59 -0.81
#> d[Individual counselling vs. Group counselling] -0.29 0.01 0.62 -1.51
#> d[Self-help vs. Group counselling] -0.62 0.01 0.71 -2.01
#> d[Self-help vs. Individual counselling] 0.16 0.02 1.07 -1.94
#> lp__ -5765.44 0.22 6.46 -5778.94
#> tau 0.93 0.01 0.23 0.59
#> 25% 50% 75%
#> d[Group counselling vs. No intervention] 0.60 1.10 1.63
#> d[Individual counselling vs. No intervention] 0.72 0.87 1.05
#> d[Self-help vs. No intervention] 0.00 0.34 0.72
#> d[Individual counselling vs. Group counselling] -0.68 -0.31 0.10
#> d[Self-help vs. Group counselling] -1.08 -0.61 -0.16
#> d[Self-help vs. Individual counselling] -0.55 0.17 0.85
#> lp__ -5769.52 -5765.31 -5760.85
#> tau 0.77 0.90 1.06
#> 97.5% n_eff Rhat
#> d[Group counselling vs. No intervention] 2.80 2493 1
#> d[Individual counselling vs. No intervention] 1.45 1136 1
#> d[Self-help vs. No intervention] 1.52 2415 1
#> d[Individual counselling vs. Group counselling] 0.96 2578 1
#> d[Self-help vs. Group counselling] 0.79 2486 1
#> d[Self-help vs. Individual counselling] 2.24 3700 1
#> lp__ -5753.63 894 1
#> tau 1.48 1129 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue Sep 23 12:39:00 2025.
#> For each parameter, n_eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor on split chains (at
#> convergence, Rhat=1).
# }