Create an mbnma.network object

Creates an object of class("mbnma.network"). Various MBNMA functions can subsequently be applied to this object.

# S3 method for mbnma.network
plot(
  x,
  level = "treatment",
  v.color = "connect",
  doselink = NULL,
  layout = igraph::in_circle(),
  remove.loops = FALSE,
  edge.scale = 1,
  v.scale = NULL,
  label.distance = 0,
  legend = TRUE,
  legend.x = "bottomleft",
  legend.y = NULL,
  ...
)

mbnma.network(data.ab, description = "Network")

Arguments

x

An object of class mbnma.network.

level

A string indicating whether nodes/facets should represent "treatment" or "agent" in the plot. Can be used to examine the expected impact of modelling dose-response in terms of network connectivity.

v.color

Can take either "connect" (the default) to indicate that nodes should only be coloured if they are connected to the network reference treatment (indicates network connectivity) or "agent" to colour nodes by agent.

doselink

If given an integer value it indicates that connections via the dose-response relationship with placebo should be plotted. The integer represents the minimum number of doses from which a dose-response function could be estimated and is equivalent to the number of parameters in the desired dose-response function plus one. If left as NULL (the default), connections to placebo via dose-response relationships will not be included.

layout

An igraph layout specification. This is a function specifying an igraph layout that determines the arrangement of the vertices (nodes). The default igraph::as_circle() arranged vertices in a circle. Two other useful layouts for network plots are: igraph::as_star(), igraph::with_fr(). Others can be found in layout_

remove.loops

A boolean value indicating whether to include loops that indicate comparisons within a node.

edge.scale

A number to scale the thickness of connecting lines (edges). Line thickness is proportional to the number of studies for a given comparison. Set to 0 to make thickness equal for all comparisons.

v.scale

A number with which to scale the size of the nodes. If the variable N (to indicate the numbers of participants in each study arm) is included in the dataset then the size of the nodes will be proportional to the number of participants within a treatment/agent in the network.

label.distance

A number scaling the distance of labels from the nodes to improve readability. The labels will be directly on top of the nodes if the default of 0 is used. Option only applicable if layout_in_circle is set to TRUE.

legend

A boolean object to indicate whether or not to plot a legend to indicate which node colour corresponds to which agent if v.color="agent". Default is TRUE.

legend.x, legend.y

The x and y co-ordinates to be used to position the legend. They can be specified by keyword or in any way which is accepted by xy.coords.

...

Options for plotting in igraph.

data.ab

A data frame of arm-level data in "long" format containing the columns:

  • studyID Study identifiers

  • dose Numeric data indicating the dose (must take positive values)

  • agent Agent identifiers (can be numeric, factor or character)

  • y Numeric data indicating the aggregate response for a continuous outcome. Required for continuous data.

  • se Numeric data indicating the standard error for a given observation. Required for continuous data.

  • r Numeric data indicating the number of responders within a study arm. Required for binomial or poisson data.

  • n Numeric data indicating the total number of participants within a study arm. Required for binomial data or when modelling Standardised Mean Differences

  • E Numeric data indicating the total exposure time for participants within a study arm. Required for poisson data.

  • class An optional column indicating a particular class code. Agents with the same identifier must also have the same class code.

  • standsd An optional column of numeric data indicating reference SDs used to standardise treatment effects when modelling using Standardised Mean Differences (SMD).

description

Optional. Short description of the network.

Value

plot(): An object of class("igraph") - any functions from the igraph package can be applied to this object to change its characteristics.

mbnma.network(): An object of class("mbnma.network") which is a list containing:

  • description A short description of the network

  • data.ab A data frame containing the arm-level network data (treatment identifiers will have been recoded to a sequential numeric code)

  • studyID A character vector with the IDs of included studies

  • agents A character vector indicating the agent identifiers that correspond to the new agent codes.

  • treatments A character vector indicating the treatment identifiers that correspond to the new treatment codes.

  • classes A character vector indicating the class identifiers (if included in the original data) that correspond to the new class codes.

Details

The S3 method plot() on an mbnma.network object generates a network plot that shows how different treatments are connected within the network via study comparisons. This can be used to identify how direct and indirect evidence are informing different treatment comparisons. Depends on igraph.

Agents/classes for arms that have dose = 0 will be relabelled as "Placebo". Missing values (NA) cannot be included in the dataset. Single arm studies cannot be included.

Methods (by generic)

  • plot(mbnma.network): Generate a network plot

Examples

# Create an mbnma.network object from the data
network <- mbnma.network(triptans)
#> Values for `agent` with dose = 0 have been recoded to `Placebo`
#> agent is being recoded to enforce sequential numbering

# Generate a network plot from the data
plot(network)


# Generate a network plot at the agent level that removes loops indicating comparisons
#within a node
plot(network, level="agent", remove.loops=TRUE)


# Generate a network plot at the treatment level that colours nodes by agent
plot(network, v.color="agent", remove.loops=TRUE)


# Generate a network plot that includes connections via the dose-response function
# For a one parameter dose-response function (e.g. exponential)
plot(network, level="treatment", doselink=1, remove.loops=TRUE)
#> Dose-response connections to placebo plotted based on a dose-response
#>                    function with 0 degrees of freedom


# For a two parameter dose-response function (e.g. Emax)
plot(network, level="treatment", doselink=2, remove.loops=TRUE)
#> Dose-response connections to placebo plotted based on a dose-response
#>                    function with 1 degrees of freedom


# Arrange network plot in a star with the reference treatment in the centre
plot(network, layout=igraph::as_star(), label.distance=3)


#### Plot a network with no placebo data included ####
# Make data with no placebo
noplac.df <- network$data.ab[network$data.ab$narm>2 & network$data.ab$agent!=1,]
net.noplac <- mbnma.network(noplac.df)
#> agent is being recoded to enforce sequential numbering

# Plotting network automatically plots connections to Placebo via dose-response
plot(net.noplac)
#> Warning: The following treatments/agents are not connected
#> to the network reference:
#> 4_1
#> 4_2

# Using the triptans headache dataset
print(triptans)
#> # A tibble: 182 × 6
#>    studyID AuthorYear              n     r  dose agent       
#>      <dbl> <fct>               <int> <dbl> <dbl> <fct>       
#>  1       1 Tfelt-Hansen P 2006    22     6     0 placebo     
#>  2       1 Tfelt-Hansen P 2006    30    14     1 sumatriptan 
#>  3       2 Goadsby PJ 2007       467   213     1 almotriptan 
#>  4       2 Goadsby PJ 2007       472   229     1 zolmitriptan
#>  5       3 Tuchman M2006         160    15     0 placebo     
#>  6       3 Tuchman M2006         174    48     1 zolmitriptan
#>  7       4 Cady G 2006           177    55     0 placebo     
#>  8       4 Cady G 2006           351   200     1 rizatriptan 
#>  9       5 Cady G 2006           167    52     0 placebo     
#> 10       5 Cady G 2006           331   195     1 rizatriptan 
#> # ℹ 172 more rows

# Define network
network <- mbnma.network(triptans, description="Example network")
#> Values for `agent` with dose = 0 have been recoded to `Placebo`
#> agent is being recoded to enforce sequential numbering

summary(network)
#> Description: Example network 
#> Number of studies: 70 
#> Number of treatments: 23 
#> Number of agents: 8 
#> Median (min, max) doses per agent (incl placebo): 4 (3, 6)
#> Agent-level network is CONNECTED 
#> 
#> Ttreatment-level network is CONNECTED 
#> 
plot(network)