Calling example("example_smk_re") will run a random effects
NMA model with the smoking cessation data, using the code in the Examples
section below. The resulting stan_nma object smk_fit_RE 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 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
#> A random effects NMA with a binomial likelihood (logit link).
#> 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% 25% 50%
#> d[Group counselling] 1.11 0.01 0.45 0.26 0.82 1.10
#> d[Individual counselling] 0.85 0.01 0.24 0.39 0.69 0.84
#> d[Self-help] 0.48 0.01 0.41 -0.30 0.22 0.48
#> lp__ -5767.79 0.19 6.42 -5781.22 -5771.84 -5767.54
#> tau 0.84 0.01 0.19 0.55 0.71 0.82
#> 75% 97.5% n_eff Rhat
#> d[Group counselling] 1.39 2.05 1472 1
#> d[Individual counselling] 1.01 1.36 969 1
#> d[Self-help] 0.74 1.32 1472 1
#> lp__ -5763.28 -5756.12 1126 1
#> tau 0.95 1.27 1089 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue Sep 23 12:38:56 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).
# }