Run MBNMA model with an Emax dose-response function (without Hill parameter) (DEPRECATED)

FUNCTION IS NOW DEPRECATED - USE mbnma.run() DIRECTLY WITH OBJECTS OF class("dosefun")

mbnma.emax(
  network,
  emax = "rel",
  ed50 = "rel",
  method = "common",
  class.effect = list(),
  UME = FALSE,
  cor = TRUE,
  omega = NULL,
  parameters.to.save = NULL,
  pd = "pd.kl",
  likelihood = NULL,
  link = NULL,
  priors = NULL,
  arg.params = NULL,
  ...
)

Arguments

network

An object of class mbnma.network.

emax

Refers to the Emax parameter of the Emax dose-response function. Can take either "rel", "common", "random", or be assigned a numeric value (see details in ?mbnma.run).

ed50

Refers to the ED50 parameter of the Emax dose-response function. Can take either "rel", "common", "random", or be assigned a numeric value (see details in ?mbnma.run).

method

Can take either "common" or "random" to indicate whether relative effects should be modelled with between-study heterogeneity or not (see details).

class.effect

A list of named strings that determines which dose-response parameters to model with a class effect and what that effect should be ("common" or "random"). Element names should match dose-response parameter names. Note that assuming class effects on some dose-response parameters may be unreasonable if the range of doses differ substantially across agents within a class.

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.

cor

A boolean object that indicates whether correlation should be modelled between relative effect dose-response parameters. This is automatically set to FALSE if class effects are modelled or if multiple dose-response functions are fitted.

omega

A scale matrix for the inverse-Wishart prior for the covariance matrix used to model the correlation between dose-response parameters (see Details for dose-response functions). omega must be a symmetric positive definite matrix with dimensions equal to the number of dose-response parameters modelled using relative effects ("rel"). If left as NULL (the default) a diagonal matrix with elements equal to 100 is used.

parameters.to.save

A character vector containing names of parameters to monitor in JAGS

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) .

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) ).

arg.params

Deprecated from version 0.4.0 onwards. Assign run and wrapper parameters

...

Arguments to be sent to R2jags.

Value

An object of S3 class(c("mbnma", "rjags")) containing parameter results from the model. Can be summarized by print() and can check traceplots using R2jags::traceplot() or various functions from the package mcmcplots.

Nodes that are automatically monitored (if present in the model) have the following interpretation:

Parameters(s)/Parameter Prefix	Interpretation
`<named dose-response parameter>` (e.g. `emax`)	The pooled effect for each dose-response parameter, as defined in dose-response functions. Will vary by agent if pooling is specified as `"rel"` in the dose-response function.
`sd`	The between-study SD (heterogeneity) for relative effects, reported if `method="random"`
`sd.<named dose-response parameter>` (e.g. `sd.emax`)	Between-study SD (heterogeneity) for absolute dose-response parameters specified as `"random"`.
`<named capitalized dose-response parameter>` (e.g. `EMAX`)	The class effect within each class for a given dose-response parameter. These will be estimated by the model if specified in `class.effects` for a given dose-response parameter.
`sd.<named capitalized dose-response parameter>` (e.g. `sd.EMAX`)	The within-class SD for different agents within the same class. Will be estimated by the model if any dose-response parameter in `class.effect` is set to `"random"`.
`totresdev`	The residual deviance of the model
`deviance`	The deviance of the model

If there are errors in the JAGS model code then the object will be a list consisting of two elements - an error message from JAGS that can help with debugging and model.arg, a list of arguments provided to mbnma.run()

which includes jagscode, the JAGS code for the model that can help users identify the source of the error.

Details

Fits a Bayesian model-based network meta-analysis (MBNMA) with a defined dose-response function. Follows the methods of Mawdsley et al. (2016) . This function acts as a wrapper for mbnma.run() that uses more clearly defined parameter names.

Dose-response parameter arguments

Argument	Model specification
`"rel"`	Implies that relative effects should be pooled for this dose-response parameter separately for each agent in the network.
`"common"`	Implies that all agents share the same common effect for this dose-response parameter.
`"random"`	Implies that all agents share a similar (exchangeable) effect for this dose-response parameter. This approach allows for modelling of variability between agents.
`numeric()`	Assigned a numeric value, indicating that this dose-response parameter should not be estimated from the data but should be assigned the numeric value determined by the user. This can be useful for fixing specific dose-response parameters (e.g. Hill parameters in Emax functions) to a single value.

References

Mawdsley D, Bennetts M, Dias S, Boucher M, Welton NJ (2016). “Model-Based Network Meta-Analysis: A Framework for Evidence Synthesis of Clinical Trial Data.” CPT Pharmacometrics Syst Pharmacol, 5(8), 393-401. ISSN 2163-8306 (Electronic) 2163-8306 (Linking), doi:10.1002/psp4.12091 , https://pubmed.ncbi.nlm.nih.gov/27479782/.

Plummer M (2008). “Penalized loss functions for Bayesian model comparison.” Biostatistics, 9(3), 523-39. ISSN 1468-4357 (Electronic) 1465-4644 (Linking), https://pubmed.ncbi.nlm.nih.gov/18209015/.

Plummer M (2017). JAGS user manual. https://people.stat.sc.edu/hansont/stat740/jags_user_manual.pdf.

Spiegelhalter DJ, Best NG, Carlin BP, van der Linde A (2002). “Bayesian measures of model complexity and fit.” J R Statistic Soc B, 64(4), 583-639.

Examples

# \donttest{
# Using the triptans data
tripnet <- mbnma.network(triptans)
#> Values for `agent` with dose = 0 have been recoded to `Placebo`
#> agent is being recoded to enforce sequential numbering and allow inclusion of `Placebo`

# Fit an Emax dose-response MBNMA with random treatment effects on Emax and ED50
emax <- mbnma.emax(tripnet, emax="rel", ed50="rel", method="random")
#> Warning: This syntax for specifying dose-response functions will be removed in future versions.
#> Please use mbnma.run() and specify functions as dosefun objects (e.g. dloglin())`
#> 'ed50' parameters are on exponential scale to ensure they take positive values on the natural scale
#> `likelihood` not given by user - set to `binomial` based on data provided
#> `link` not given by user - set to `logit` based on assigned value for `likelihood`
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 182
#>    Unobserved stochastic nodes: 191
#>    Total graph size: 4135
#> 
#> Initializing model
#> 

# Fit an Emax dose-response MBNMA with common treatment effects on Emax and
#a single common parameter estimated for ED50
emax <- mbnma.emax(tripnet, emax="rel", ed50="common", method="common")
#> Warning: This syntax for specifying dose-response functions will be removed in future versions.
#> Please use mbnma.run() and specify functions as dosefun objects (e.g. dloglin())`
#> 'ed50' parameters are on exponential scale to ensure they take positive values on the natural scale
#> `likelihood` not given by user - set to `binomial` based on data provided
#> `link` not given by user - set to `logit` based on assigned value for `likelihood`
#> Compiling model graph
#>    Resolving undeclared variables
#>    Allocating nodes
#> Graph information:
#>    Observed stochastic nodes: 182
#>    Unobserved stochastic nodes: 78
#>    Total graph size: 3665
#> 
#> Initializing model
#> 

# For further examples see ?mbnma.run
# }