Run an NMA model — plot.nma • MBNMAdose

Used for calculating treatment-level NMA results, either when comparing MBNMA models to models that make no assumptions regarding dose-response , or to estimate split results for overlay.split. Results can also be compared between consistency (UME=FALSE) and inconsistency (UME=TRUE) models to test the validity of the consistency assumption at the treatment-level.

# S3 method for nma
plot(x, bydose = TRUE, scales = "free_x", ...)

nma.run(
  network,
  method = "common",
  likelihood = NULL,
  link = NULL,
  priors = NULL,
  sdscale = FALSE,
  warn.rhat = TRUE,
  n.iter = 20000,
  drop.discon = TRUE,
  UME = FALSE,
  pd = "pd.kl",
  parameters.to.save = NULL,
  ...
)

Arguments

x

An object of class("nma")

bydose

A boolean object indicating whether to plot responses with dose on the x-axis (TRUE) to be able to examine potential dose-response shapes, or to plot a conventional forest plot with all treatments on the same plot (FALSE)

scales

Should scales be fixed ("fixed", the default), free ("free"), or free in one dimension ("free_x", "free_y")?

...

Arguments to be sent to ggplot2::ggplot()

network

An object of class mbnma.network.

method

Indicates the type of split (treatment-level) NMA to perform when overlay.split=TRUE. Can take either "common" or "random".

likelihood

A string indicating the likelihood to use in the model. Can take either "binomial", "normal" or "poisson". If left as NULL the likelihood will be inferred from the data.

link

A string indicating the link function to use in the model. Can take any link function defined within JAGS (e.g. "logit", "log", "probit", "cloglog"), be assigned the value "identity" for an identity link function, or be assigned the value "smd" for modelling Standardised Mean Differences using an identity link function. If left as NULL the link function will be automatically assigned based on the likelihood.

priors

A named list of parameter values (without indices) and replacement prior distribution values given as strings using distributions as specified in JAGS syntax (see Plummer (2017) ). Note that normal distributions in JAGS are specified as $$N(\mu, prec)$$, where $$prec = 1 / {\sigma^2}$$.

sdscale

Logical object to indicate whether to write a model that specifies a reference SD for standardising when modelling using Standardised Mean Differences. Specifying sdscale=TRUE will therefore only modify the model if link function is set to SMD (link="smd").

warn.rhat

A boolean object to indicate whether to return a warning if Rhat values for any monitored parameter are >1.02 (suggestive of non-convergence).

n.iter

number of total iterations per chain (including burn in; default: 20000)

drop.discon

A boolean object that indicates whether or not to drop disconnected studies from the network.

UME

A boolean object to indicate whether to fit an Unrelated Mean Effects model that does not assume consistency and so can be used to test if the consistency assumption is valid.

pd

Can take either:

pv only pV will be reported (as automatically outputted by R2jags).
plugin calculates pD by the plug-in method (Spiegelhalter et al. 2002) . It is faster, but may output negative non-sensical values, due to skewed deviances that can arise with non-linear models.
pd.kl calculates pD by the Kullback-Leibler divergence (Plummer 2008) . This will require running the model for additional iterations but is a more robust calculation for the effective number of parameters in non-linear models.
popt calculates pD using an optimism adjustment which allows for calculation of the penalized expected deviance (Plummer 2008) .

parameters.to.save

A character vector containing names of parameters to monitor in JAGS

Functions

plot(nma): Plot outputs from treatment-level NMA models

Results can be plotted either as a single forest plot, or facetted by agent and plotted with increasing dose in order to identify potential dose-response relationships. If Placebo (or any agents with dose=0) is included in the network then this will be used as the reference treatment, but if it is not then results will be plotted versus the network reference used in the NMA object (x).

Examples

# \donttest{
# Run random effects NMA on the alogliptin dataset
alognet <- mbnma.network(alog_pcfb)
#> Values for `agent` with dose = 0 have been recoded to `Placebo`
#> agent is being recoded to enforce sequential numbering
nma <- nma.run(alognet, method="random")
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 46
#>    Unobserved stochastic nodes: 52
#>    Total graph size: 641
#> 
#> Initializing model
#> 
print(nma)
#> $jagsresult
#> Inference for Bugs model at "/tmp/RtmpAwWCVP/file17af67712332", fit using jags,
#>  3 chains, each with 20000 iterations (first 10000 discarded), n.thin = 10
#>  n.sims = 3000 iterations saved
#>            mu.vect sd.vect     2.5%      25%      50%      75%    97.5%  Rhat
#> d[1]         0.000   0.000    0.000    0.000    0.000    0.000    0.000 1.000
#> d[2]        -0.456   0.090   -0.627   -0.517   -0.457   -0.396   -0.271 1.001
#> d[3]        -0.655   0.045   -0.742   -0.684   -0.654   -0.625   -0.562 1.002
#> d[4]        -0.710   0.045   -0.800   -0.738   -0.710   -0.681   -0.620 1.001
#> d[5]        -0.759   0.084   -0.916   -0.815   -0.761   -0.704   -0.585 1.001
#> d[6]        -0.684   0.169   -1.017   -0.794   -0.686   -0.576   -0.340 1.001
#> sd           0.124   0.028    0.074    0.104    0.122    0.140    0.185 1.001
#> totresdev   46.923   9.682   30.268   39.944   46.113   52.898   68.582 1.001
#> deviance  -124.391   9.682 -141.046 -131.370 -125.201 -118.416 -102.732 1.001
#>           n.eff
#> d[1]          1
#> d[2]       3000
#> d[3]       1500
#> d[4]       3000
#> d[5]       3000
#> d[6]       2000
#> sd         3000
#> totresdev  3000
#> deviance   3000
#> 
#> For each parameter, n.eff is a crude measure of effective sample size,
#> and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
#> 
#> DIC info (using the rule, pD = var(deviance)/2)
#> pD = 36.9 and DIC = -88.3
#> DIC is an estimate of expected predictive error (lower deviance is better).
#> 
#> $trt.labs
#> [1] "Placebo_0"       "alogliptin_6.25" "alogliptin_12.5" "alogliptin_25"  
#> [5] "alogliptin_50"   "alogliptin_100" 
#> 
#> $UME
#> [1] FALSE
#> 
#> attr(,"class")
#> [1] "nma"
plot(nma)


# Run common effects NMA keeping treatments that are disconnected in the NMA
goutnet <- mbnma.network(gout)
#> Values for `agent` with dose = 0 have been recoded to `Placebo`
#> agent is being recoded to enforce sequential numbering
nma <- nma.run(goutnet, method="common", drop.discon=FALSE)
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 27
#>    Unobserved stochastic nodes: 28
#>    Total graph size: 296
#> 
#> Initializing model
#> 

# Run an Unrelated Mean Effects (UME) inconsistency model on triptans dataset
tripnet <- mbnma.network(triptans)
#> Values for `agent` with dose = 0 have been recoded to `Placebo`
#> agent is being recoded to enforce sequential numbering
ume <- nma.run(tripnet, method="random", UME=TRUE)
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 182
#>    Unobserved stochastic nodes: 436
#>    Total graph size: 4072
#> 
#> Initializing model
#> 
#> Warning: error/missing in parameter d in parameters.to.save, 
#>    Be aware of the output results.
# }