library(multinma)
options(mc.cores = parallel::detectCores())
#> For execution on a local, multicore CPU with excess RAM we recommend calling
#> options(mc.cores = parallel::detectCores())
#>
#> Attaching package: 'multinma'
#> The following objects are masked from 'package:stats':
#>
#> dgamma, pgamma, qgamma
This vignette describes the analysis of smoking cessation data (Hasselblad 1998), replicating the
analysis in NICE Technical Support Document 4 (Dias et al. 2011). The
data are available in this package as smoking
:
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
We begin by setting up the network. We have arm-level count data
giving the number quitting smoking (r
) out of the total
(n
) in each arm, so we use the function
set_agd_arm()
. Treatment “No intervention” is set as the
network reference treatment.
<- set_agd_arm(smoking,
smknet study = studyn,
trt = trtc,
r = r,
n = n,
trt_ref = "No intervention")
smknet#> 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
Plot the network structure.
plot(smknet, weight_edges = TRUE, weight_nodes = TRUE)
Following TSD 4, we fit a random effects NMA model, using the
nma()
function with trt_effects = "random"
. We
use \(\mathrm{N}(0, 100^2)\) prior
distributions for the treatment effects \(d_k\) and study-specific intercepts \(\mu_j\), and a \(\textrm{half-N}(5^2)\) prior distribution
for the between-study heterogeneity standard deviation \(\tau\). We can examine the range of
parameter values implied by these prior distributions with the
summary()
method:
summary(normal(scale = 100))
#> A Normal prior distribution: location = 0, scale = 100.
#> 50% of the prior density lies between -67.45 and 67.45.
#> 95% of the prior density lies between -196 and 196.
summary(half_normal(scale = 5))
#> A half-Normal prior distribution: location = 0, scale = 5.
#> 50% of the prior density lies between 0 and 3.37.
#> 95% of the prior density lies between 0 and 9.8.
The model is fitted using the nma()
function. By
default, this will use a Binomial likelihood and a logit link function,
auto-detected from the data.
<- nma(smknet,
smkfit trt_effects = "random",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 100),
prior_het = normal(scale = 5))
Basic parameter summaries are given by the print()
method:
smkfit#> 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% 75% 97.5% n_eff
#> d[Group counselling] 1.11 0.01 0.44 0.27 0.81 1.09 1.40 2.01 1998
#> d[Individual counselling] 0.84 0.01 0.25 0.39 0.67 0.83 0.99 1.36 1206
#> d[Self-help] 0.50 0.01 0.40 -0.27 0.24 0.48 0.75 1.31 1916
#> lp__ -5919.72 0.20 6.50 -5933.29 -5923.82 -5919.41 -5915.14 -5907.86 1012
#> tau 0.84 0.01 0.19 0.54 0.70 0.82 0.95 1.28 918
#> Rhat
#> d[Group counselling] 1
#> d[Individual counselling] 1
#> d[Self-help] 1
#> lp__ 1
#> tau 1
#>
#> Samples were drawn using NUTS(diag_e) at Tue Jan 9 17:49:33 2024.
#> 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).
By default, summaries of the study-specific intercepts \(\mu_j\) and study-specific relative effects
\(\delta_{jk}\) are hidden, but could
be examined by changing the pars
argument:
# Not run
print(smkfit, pars = c("d", "tau", "mu", "delta"))
The prior and posterior distributions can be compared visually using
the plot_prior_posterior()
function:
plot_prior_posterior(smkfit)
By default, this displays all model parameters given prior
distributions (in this case \(d_k\),
\(\mu_j\), and \(\tau\)), but this may be changed using the
prior
argument:
plot_prior_posterior(smkfit, prior = "het")
Model fit can be checked using the dic()
function
<- dic(smkfit))
(dic_consistency #> Residual deviance: 54 (on 50 data points)
#> pD: 43.7
#> DIC: 97.7
and the residual deviance contributions examined with the
corresponding plot()
method
plot(dic_consistency)
Overall model fit seems to be adequate, with almost all points showing good fit (mean residual deviance contribution of 1). The only two points with higher residual deviance (i.e. worse fit) correspond to the two zero counts in the data:
$r == 0, ]
smoking[smoking#> studyn trtn trtc r n
#> 13 6 1 No intervention 0 33
#> 31 15 1 No intervention 0 20
Note: The results of the inconsistency models here are slightly different to those of Dias et al. (2010, 2011), although the overall conclusions are the same. This is due to the presence of multi-arm trials and a different ordering of treatments, meaning that inconsistency is parameterised differently within the multi-arm trials. The same results as Dias et al. are obtained if the network is instead set up with
trtn
as the treatment variable.
Another method for assessing inconsistency is node-splitting (Dias et al. 2011, 2010). Whereas the UME model assesses inconsistency globally, node-splitting assesses inconsistency locally for each potentially inconsistent comparison (those with both direct and indirect evidence) in turn.
Node-splitting can be performed using the nma()
function
with the argument consistency = "nodesplit"
. By default,
all possible comparisons will be split (as determined by the
get_nodesplits()
function). Alternatively, a specific
comparison or comparisons to split can be provided to the
nodesplit
argument.
<- nma(smknet,
smk_nodesplit consistency = "nodesplit",
trt_effects = "random",
prior_intercept = normal(scale = 100),
prior_trt = normal(scale = 100),
prior_het = normal(scale = 5))
#> Fitting model 1 of 7, node-split: Group counselling vs. No intervention
#> Fitting model 2 of 7, node-split: Individual counselling vs. No intervention
#> Fitting model 3 of 7, node-split: Self-help vs. No intervention
#> Fitting model 4 of 7, node-split: Individual counselling vs. Group counselling
#> Fitting model 5 of 7, node-split: Self-help vs. Group counselling
#> Fitting model 6 of 7, node-split: Self-help vs. Individual counselling
#> Fitting model 7 of 7, consistency model
The summary()
method summarises the node-splitting
results, displaying the direct and indirect estimates \(d_\mathrm{dir}\) and \(d_\mathrm{ind}\) from each node-split
model, the network estimate \(d_\mathrm{net}\) from the consistency
model, the inconsistency factor \(\omega =
d_\mathrm{dir} - d_\mathrm{ind}\), and a Bayesian \(p\)-value for inconsistency on each
comparison. Since random effects models are fitted, the heterogeneity
standard deviation \(\tau\) under each
node-split model and under the consistency model is also displayed. The
DIC model fit statistics are also provided.
summary(smk_nodesplit)
#> Node-splitting models fitted for 6 comparisons.
#>
#> ------------------------------ Node-split Group counselling vs. No intervention ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net 1.09 0.44 0.28 0.79 1.07 1.37 1.97 1793 2275 1
#> d_dir 1.06 0.75 -0.36 0.55 1.03 1.54 2.63 3096 2875 1
#> d_ind 1.12 0.55 -0.01 0.76 1.12 1.48 2.19 1517 1709 1
#> omega -0.05 0.91 -1.81 -0.66 -0.08 0.54 1.82 2295 2580 1
#> tau 0.87 0.20 0.56 0.73 0.84 0.98 1.32 1342 1858 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.94 1.29 1092 1786 1
#>
#> Residual deviance: 53.9 (on 50 data points)
#> pD: 44.1
#> DIC: 98
#>
#> Bayesian p-value: 0.93
#>
#> ------------------------- Node-split Individual counselling vs. No intervention ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net 0.84 0.24 0.38 0.68 0.83 0.99 1.36 1176 1770 1.00
#> d_dir 0.89 0.26 0.40 0.72 0.88 1.05 1.42 1218 1860 1.00
#> d_ind 0.56 0.68 -0.74 0.12 0.55 0.98 1.99 1165 1674 1.00
#> omega 0.33 0.70 -1.08 -0.10 0.35 0.79 1.65 1163 1632 1.00
#> tau 0.86 0.19 0.55 0.72 0.83 0.97 1.28 1118 2046 1.01
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.94 1.29 1092 1786 1.00
#>
#> Residual deviance: 54.2 (on 50 data points)
#> pD: 44.1
#> DIC: 98.3
#>
#> Bayesian p-value: 0.6
#>
#> -------------------------------------- Node-split Self-help vs. No intervention ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net 0.48 0.40 -0.28 0.21 0.48 0.73 1.28 1698 1876 1
#> d_dir 0.33 0.54 -0.71 -0.01 0.33 0.68 1.41 3339 2529 1
#> d_ind 0.70 0.65 -0.58 0.28 0.69 1.11 2.03 2165 2405 1
#> omega -0.36 0.83 -2.09 -0.88 -0.35 0.17 1.26 2351 2589 1
#> tau 0.87 0.19 0.56 0.73 0.84 0.98 1.31 1311 2303 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.94 1.29 1092 1786 1
#>
#> Residual deviance: 53.7 (on 50 data points)
#> pD: 44.1
#> DIC: 97.8
#>
#> Bayesian p-value: 0.64
#>
#> ----------------------- Node-split Individual counselling vs. Group counselling ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net -0.25 0.41 -1.06 -0.52 -0.24 0.03 0.55 2718 2818 1
#> d_dir -0.10 0.51 -1.09 -0.43 -0.10 0.23 0.90 4213 3127 1
#> d_ind -0.53 0.61 -1.76 -0.92 -0.51 -0.14 0.67 1866 2406 1
#> omega 0.43 0.68 -0.88 -0.02 0.42 0.86 1.78 1934 1802 1
#> tau 0.87 0.20 0.56 0.73 0.84 0.98 1.35 1341 1985 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.94 1.29 1092 1786 1
#>
#> Residual deviance: 54 (on 50 data points)
#> pD: 44.4
#> DIC: 98.5
#>
#> Bayesian p-value: 0.52
#>
#> ------------------------------------ Node-split Self-help vs. Group counselling ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net -0.61 0.49 -1.58 -0.93 -0.61 -0.30 0.36 2737 2551 1
#> d_dir -0.63 0.66 -1.98 -1.05 -0.63 -0.21 0.68 3657 3014 1
#> d_ind -0.62 0.67 -2.05 -1.04 -0.61 -0.18 0.70 2132 2344 1
#> omega -0.02 0.88 -1.75 -0.58 -0.02 0.56 1.72 2421 2701 1
#> tau 0.86 0.19 0.57 0.72 0.84 0.97 1.29 1013 1808 1
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.94 1.29 1092 1786 1
#>
#> Residual deviance: 54 (on 50 data points)
#> pD: 44.2
#> DIC: 98.2
#>
#> Bayesian p-value: 0.99
#>
#> ------------------------------- Node-split Self-help vs. Individual counselling ----
#>
#> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> d_net -0.36 0.40 -1.15 -0.62 -0.37 -0.11 0.45 2188 2043 1.00
#> d_dir 0.06 0.65 -1.24 -0.36 0.06 0.50 1.35 3068 2587 1.00
#> d_ind -0.61 0.51 -1.62 -0.94 -0.61 -0.28 0.38 1883 2496 1.00
#> omega 0.68 0.80 -0.91 0.15 0.67 1.20 2.27 2164 2423 1.00
#> tau 0.86 0.19 0.56 0.72 0.83 0.96 1.30 1066 1156 1.01
#> tau_consistency 0.84 0.19 0.55 0.71 0.82 0.94 1.29 1092 1786 1.00
#>
#> Residual deviance: 53.8 (on 50 data points)
#> pD: 44.1
#> DIC: 97.9
#>
#> Bayesian p-value: 0.38
The DIC of each inconsistency model is unchanged from the consistency model, no node-splits result in reduced heterogeneity standard deviation \(\tau\) compared to the consistency model, and the Bayesian \(p\)-values are all large. There is no evidence of inconsistency.
We can visually compare the posterior distributions of the direct,
indirect, and network estimates using the plot()
method.
These are all in agreement; the posterior densities of the direct and
indirect estimates overlap. Notice that there is not much indirect
information for the Individual counselling vs. No intervention
comparison, so the network (consistency) estimate is very similar to the
direct estimate for this comparison.
plot(smk_nodesplit) +
::theme(legend.position = "bottom", legend.direct = "horizontal") ggplot2
Pairwise relative effects, for all pairwise contrasts with
all_contrasts = TRUE
.
<- relative_effects(smkfit, all_contrasts = TRUE))
(smk_releff #> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS
#> d[Group counselling vs. No intervention] 1.11 0.44 0.27 0.81 1.09 1.40 2.01 2031
#> d[Individual counselling vs. No intervention] 0.84 0.25 0.39 0.67 0.83 0.99 1.36 1221
#> d[Self-help vs. No intervention] 0.50 0.40 -0.27 0.24 0.48 0.75 1.31 1930
#> d[Individual counselling vs. Group counselling] -0.27 0.42 -1.13 -0.54 -0.27 0.01 0.54 2773
#> d[Self-help vs. Group counselling] -0.61 0.50 -1.58 -0.92 -0.61 -0.29 0.37 2816
#> d[Self-help vs. Individual counselling] -0.34 0.41 -1.15 -0.61 -0.35 -0.08 0.47 2229
#> Tail_ESS Rhat
#> d[Group counselling vs. No intervention] 2307 1
#> d[Individual counselling vs. No intervention] 1875 1
#> d[Self-help vs. No intervention] 2362 1
#> d[Individual counselling vs. Group counselling] 2754 1
#> d[Self-help vs. Group counselling] 2787 1
#> d[Self-help vs. Individual counselling] 2425 1
plot(smk_releff, ref_line = 0)
Treatment rankings, rank probabilities, and cumulative rank
probabilities. We set lower_better = FALSE
since a higher
log odds of cessation is better (the outcome is positive).
<- posterior_ranks(smkfit, lower_better = FALSE))
(smk_ranks #> mean sd 2.5% 25% 50% 75% 97.5% Bulk_ESS Tail_ESS Rhat
#> rank[No intervention] 3.90 0.31 3 4 4 4 4 2465 NA 1
#> rank[Group counselling] 1.36 0.63 1 1 1 2 3 2834 2832 1
#> rank[Individual counselling] 1.94 0.63 1 2 2 2 3 2409 2647 1
#> rank[Self-help] 2.80 0.69 1 3 3 3 4 2581 NA 1
plot(smk_ranks)
<- posterior_rank_probs(smkfit, lower_better = FALSE))
(smk_rankprobs #> p_rank[1] p_rank[2] p_rank[3] p_rank[4]
#> d[No intervention] 0.00 0.00 0.10 0.9
#> d[Group counselling] 0.71 0.22 0.07 0.0
#> d[Individual counselling] 0.23 0.60 0.17 0.0
#> d[Self-help] 0.06 0.18 0.67 0.1
plot(smk_rankprobs)
<- posterior_rank_probs(smkfit, lower_better = FALSE, cumulative = TRUE))
(smk_cumrankprobs #> p_rank[1] p_rank[2] p_rank[3] p_rank[4]
#> d[No intervention] 0.00 0.00 0.1 1
#> d[Group counselling] 0.71 0.93 1.0 1
#> d[Individual counselling] 0.23 0.83 1.0 1
#> d[Self-help] 0.06 0.24 0.9 1
plot(smk_cumrankprobs)