This vignette aims to illustrate the toolkits of the
**rnmamod** package for the creation of the network plot
and summarisation of the corresponding outcome data. If missing
participant outcome data (MOD) have been extracted for all trials of the
dataset, the **rnmamod** package facilitates visualising
the proportion of MOD across the network and within the dataset.

We will use the systematic review of Bottomley et al.,
(2011) that comprises 9 trials comparing six pharmacologic
interventions with each other and placebo for moderately severe scalp
psoriasis. The analysed binary outcome is the investigator global
assessment response at 4 weeks (`?nma.bottomley2011`

).

```
study t1 t2 t3 t4 r1 r2 r3 r4 m1 m2 m3 m4 n1 n2 n3 n4
1 Buckley, 2008 1 6 NA NA 67 79 NA NA 2 1 NA NA 110 108 NA NA
2 Tyring, 2008 6 7 NA NA 74 12 NA NA 2 0 NA NA 135 42 NA NA
3 Kragballe, 2009 3 6 NA NA 19 114 NA NA 9 2 NA NA 105 207 NA NA
4 Luger, 2008 3 6 NA NA 101 196 NA NA 44 9 NA NA 431 419 NA NA
5 Klaber, 1994 2 3 NA NA 175 138 NA NA 2 11 NA NA 234 240 NA NA
6 Barrett, 2005 3 4 NA NA 79 79 NA NA 19 18 NA NA 225 236 NA NA
7 Klaber and McKinnon, 2000 3 5 NA NA 55 31 NA NA 35 16 NA NA 238 237 NA NA
8 van de Kerkhof, 2009 1 3 6 NA 287 74 311 NA 7 8 4 NA 563 286 568 NA
9 Jemec, 2008 1 3 6 7 304 64 362 20 6 20 8 7 556 272 541 136
```

The dataset has the one-trial-per-row format containing arm-level data for each trial. This format is widely used for BUGS models. For a binary outcome, the dataset must have a minimum of three items:

- item
`t`

that refers to the intervention identifier for the corresponding (intervention) arm; - item
`r`

that refers to the number of observed events in the corresponding arm. - item
`n`

that refers to the number of randomised participants in the corresponding arm.

If there is at least one trial that reports the number of missing
participants per arm, we also include the item `m`

in the
dataset. If a trial reports the *total* number of missing
participants rather than the number of missing participants per arm, we
indicate with `NA`

in the item `m`

the arms of the
corresponding trial.

In the example, the maximum number of interventions observed in a
trial is four. Therefore, each element comprises four columns (e.g.,
`t1`

, `t2`

, `t3`

, `t4`

) to
indicate the maximum number of arms in the dataset. Furthermore, all
trials of the dataset reported the number of missing participants per
arm; therefore, the element `m`

appears in the dataset.

The function `netplot`

(see ?netplot for help) creates the
network plot using only two arguments: the `data`

for the
dataset (in one-trial-per-row format) and `drug_names`

for
the names of each intervention in the dataset. To obtain the network
plot, `netplot`

calls the `nma.networkplot`

function from the **pcnetmeta** package.

`netplot(data = nma.bottomley2011, drug_names = c("BDP", "BMV", "CPL", "CPL+polytar", "capasal", "TCF gel", "placebo"))`

The intervention names in `drug_names`

must be sorted in
the ascending order of their identifier. Hence, `1`

in the
element `t`

refers to `BDP`

, (betamethasone
dipropionate) `2`

to `BMV`

(betamethasone
valerate), `3`

to `CPL`

(calcipotriol) and so on.
See Details in `?nma.bottomley20119`

for the names of the
interventions.

Each node refers to an intervention and each edge refers to a pairwise comparison. The size of a node and the thickness of an edge are weighted by the number of trials that investigated the corresponding intervention and pairwise comparison, respectively.

`netplot`

also produces a table with the characteristics
of the network, such as the number of interventions, number of possible
comparisons, number of direct comparisons (i.e., comparisons of
interventions informed by at least one trial), and so on:

Characteristic | Total |
---|---|

Interventions | 7 |

Possible comparisons | 21 |

Direct comparisons | 9 |

Indirect comparisons | 12 |

Trials | 9 |

Two-arm trials | 7 |

Multi-arm trials | 2 |

Randomised participants | 5889 |

Proportion of completers | 96 |

Proportion of observed events | 47 |

Trials with at least one zero event | 0 |

Trials with all zero events | 0 |

Furthermore, `netplot`

returns a table that summarises the
number of trials, number of randomised participants and the proportion
of completers (participants who completed the trial) **per
intervention**. In the case of a binary outcome, the table
additionally illustrates the distribution of the outcome as proportion
across the corresponding trials:

Interventions | Total trials | Total randomised | Completers (%) | Total events (%) | Min. events (%) | Median events (%) | Max. events (%) |
---|---|---|---|---|---|---|---|

BDP | 3 | 1229 | 99 | 54 | 52 | 55 | 62 |

BMV | 1 | 234 | 99 | 75 | 75 | 75 | 75 |

CPL | 7 | 1797 | 92 | 32 | 20 | 27 | 60 |

CPL+polytar | 1 | 236 | 92 | 36 | 36 | 36 | 36 |

capasal | 1 | 237 | 93 | 14 | 14 | 14 | 14 |

TCF gel | 6 | 1978 | 99 | 58 | 48 | 56 | 74 |

placebo | 2 | 178 | 96 | 19 | 16 | 22 | 29 |

An identical table is returned for the **observed
comparisons** in the network:

Comparisons | Total trials | Total randomised | Completers (%) | Total events (%) | Min. events (%) | Median events (%) | Max. events (%) |
---|---|---|---|---|---|---|---|

CPL vs BDP | 2 | 1677 | 98 | 45 | 43 | 45 | 46 |

CPL vs BMV | 1 | 474 | 97 | 68 | 68 | 68 | 68 |

CPL+polytar vs CPL | 1 | 461 | 92 | 37 | 37 | 37 | 37 |

TCF gel vs BDP | 3 | 2446 | 99 | 58 | 53 | 61 | 68 |

TCF gel vs CPL | 4 | 2829 | 96 | 46 | 37 | 45 | 54 |

capasal vs CPL | 1 | 475 | 89 | 20 | 20 | 20 | 20 |

placebo vs BDP | 1 | 692 | 98 | 48 | 48 | 48 | 48 |

placebo vs CPL | 1 | 408 | 93 | 22 | 22 | 22 | 22 |

placebo vs TCF gel | 2 | 854 | 98 | 56 | 49 | 53 | 58 |

The users can export all tables in xlsx file at the working directory
of their project by adding the argument `save_xls = TRUE`

in
the `netplot`

function.

When missing participants have been reported for *each arm of
every* trial, we use the `heatmap_missing_network`

function to illustrate the distribution of the proportion of missing
participants **per intervention** (main diagonal) and
**observed comparison** (lower off-diagonal) in the network
(see Details in `?heatmap_missing_network`

).

`heatmap_missing_network(data = nma.bottomley2011, drug_names = c("BDP", "BMV", "CPL", "CPL+polytar", "capasal", "TCF gel", "placebo"))`

The **green** colour
implies a median proportion of missing participant up to 5%, and hence,
a **low risk**
associated with the missing participants. The **red** colour implies a
median proportion of missing participant over 20%, and hence, a **high risk** associated with
the missing participants; otherwise, **orange** indicates a **moderate risk**.

In the example, most of the interventions and observed comparisons
were associated with a *low* risk due the participant losses.

Use the `heatmap_missing_dataset`

function To illustrate
the proportion of missing participants in each arm of every trial in the
dataset :

`heatmap_missing_dataset(data = nma.bottomley2011, trial_names = nma.bottomley2011$study, drug_names = c("BDP", "BMV", "CPL", "CPL+polytar", "capasal", "TCF gel", "placebo"))`

Bottomley JM, Taylor RS, Ryttov J. The effectiveness of two-compound
formulation calcipotriol and betamethasone dipropionate gel in the
treatment of moderately severe scalp psoriasis: a systematic review of
direct and indirect evidence. *Curr Med Res Opin*
2011;**27**(1):251–268. doi:
10.1185/03007995.2010.541022

Lifeng Lin, Jing Zhang, James S. Hodges, Haitao Chu. Performing
Arm-Based Network Meta-Analysis in R with the pcnetmeta Package. *J
Stat Softw* 2017; **80**(5):1–25. doi:
10.18637/jss.v080.i05