Package *rankinma* supports users to easily obtain and
visualize various metrics of treatment ranking from network
meta-analysis no matter using frequentist or Bayesian approach. This
package not only accepts manual-prepared data set of treatment ranking
metrics from users, but also can help users to gather various treatment
ranking metrics in network meta-analysis. Users can use functions in
*rankinma* by calling the library with following syntax:

`library(rankinma)`

*rankinma* allows users to visualize various treatment ranking
metrics in network meta-analysis based either common-effect model or
random-effects model. The current version includes three common metrics
of treatment ranking.

**Probabilities:**probabilities of every available treatment on each possible rank.**SUCRA:**the value of surface under the cumulative ranking curve using Bayesian approach.**P-score:**the value of SUCRA using frequentist approach.

Briefly, *rankinma* can be used for visualization of both
detailed metrics of probabilities and global metrics (i.e. SUCRA and
P-score). Besides, *rankinma* provides users multiple types of
plots to illustrate aforementioned treatment ranking metrics, and
current version consists of five types of plots with six sub-types.

**Beading plot:**a novel graphics for displaying global metrics of treatment ranking (i.e. SUCRA, P-score, and P-best) based on numeric line plot.**Bar chart:**a classic graphics for most metrics of treatment ranking (i.e. probabilities, SUCRA, and P-score), and*rankinma*supports two sub-type of bar chart in terms of side-by-side bar chart and cumulative bar chart.**Line chart:**a classic graphics for most metrics of treatment ranking (i.e. probabilities, SUCRA, and P-score), and*rankinma*supports two sub-type of line chart in terms of simple line chart (a line on a chart) and composite line chart (multiple lines on a chart).**Heat plot:**a new graphics for showing global metrics of treatment ranking (i.e. SUCRA and P-score) for each outcome, and*rankinma*supports to gather all heat plots of outcomes with interests on a plot.**Spie chart:**a new graphics proposed in 2020 for displaying multiple global metrics of treatment ranking (i.e. SUCRA and P-score) from outcomes with interests by each treatment, and*rankinma*supports to place all spie charts on a plot.

Users can visualize treatment ranking after network meta-analysis in
five steps, but have to check condition before using
*rankinma*.

**Situation 1:** Users have data for network
meta-analysis of **a single outcome** but do not get
treatment ranking metrics yet.

**Situation 2:** Users have data for network
meta-analysis of **various outcomes** but do not get
treatment ranking metrics yet.

**Step 1.** Load data and do network meta-analysis.

**Step 2.** Get treatment ranking metrics from the
network meta-analysis using function `GetMetrics()`

.

**Step 3.** Setup data in *rankinma* format using
function `SetMetrics()`

.

**Step 4.** Visualization using function
`PlotBeads()`

, `PlotHeat()`

,
`PlotBar()`

, or `PlotLine()`

.

**Step 1.** Load data and do network meta-analysis.

**Step 2.** Get treatment ranking metrics from the
network meta-analysis using function `GetMetrics()`

.

— Repeat step 1 and 2 for each outcome, and keep output of them for the further steps. —

**Step 3.** Combine treatment ranking metrics using
function `rbind()`

in R *base*.

**Step 4.** Setup data in *rankinma* format using
function `SetMetrics()`

.

**Step 5.** Visualization using function
`PlotBeads()`

, `PlotHeat()`

,
`PlotBar()`

, or `PlotLine()`

.

The following steps and syntax demonstrate how user can illustrate a summary of treatment ranking metrics on various outcomes from network meta-analysis.

Example 1 for illustrating line chart when users have data for network meta-analysis of a single outcome but do not get treatment ranking metrics yet.

**STEP 1.** Load data

```
library(rankinma)
library(netmeta)
data(Senn2013)
<- netmeta(TE,
nmaOutput
seTE,
treat1,
treat2,
studlab, data = Senn2013,
sm = "SMD")
```

**STEP 2.** Get Probabilities

```
<- GetMetrics(nmaOutput,
dataMetrics outcome = "HbA1c.random",
prefer = "small",
metrics = "Probabilities",
model = "random",
simt = 1000)
```

**STEP 3.** Set data for rankinma

```
<- SetMetrics(dataMetrics,
dataRankinma tx = tx,
outcome = outcome,
metrics.name = "Probabilities")
```

**STEP 4.** Visualize the probabilities of treatments
among possible ranks

If the users wish to visualize the information using line chart, the following syntax could be a reference.

```
PlotLine(data = dataRankinma,
compo = TRUE)
```

If the users wish to visualize the information using stacked bar chart, the following syntax could be a reference.

```
PlotBar(data = dataRankinma,
accum = TRUE)
```

Example 2 for illustrating beading plot when users have data for network meta-analysis of multiple outcomes but do not get treatment ranking metrics yet.

**STEP 1.** Load data

```
library(rankinma)
library(netmeta)
data(Senn2013)
<- netmeta(TE,
nmaOutput
seTE,
treat1,
treat2,
studlab, data = Senn2013,
sm = "SMD")
```

**STEP 2.** Get global metrics Due to several rank
metrics in network meta-analysis, users have to decide which global
metric would be used in the analysis; accordingly users choose argument
for parameter `metrics`

in the function
`GetMetrics()`

. The following syntax is for users who would
like to obtain the Surface Under the Cumulative Ranking Curve
(SUCRA).

*Get SUCRA*

```
<- GetMetrics(nmaOutput,
nmaRandom outcome = "HbA1c.random",
prefer = "small",
metrics = "SUCRA",
model = "random",
simt = 1000)
<- GetMetrics(nmaOutput,
nmaCommon outcome = "HbA1c.common",
prefer = "small",
metrics = "SUCRA",
model = "common",
simt = 1000)
```

If users wish to obtain the P-score, they can utilize the following syntax.

*Get P-score*

```
<- GetMetrics(nmaOutput,
nmaRandom outcome = "HbA1c.random",
prefer = "small",
metrics = "P-score",
model = "random",
simt = 1000)
<- GetMetrics(nmaOutput,
nmaCommon outcome = "HbA1c.common",
prefer = "small",
metrics = "P-score",
model = "common",
simt = 1000)
```

If users wish to obtain the P-best, they can assign the argument
`P-best`

to the parameter `metrics`

in the
function `GetMetrics()`

. The following syntax is for
reference.

*Get P-best*

```
<- GetMetrics(nmaOutput,
nmaRandom outcome = "HbA1c.random",
prefer = "small",
metrics = "P-best",
model = "random",
simt = 1000)
<- GetMetrics(nmaOutput,
nmaCommon outcome = "HbA1c.common",
prefer = "small",
metrics = "P-best",
model = "common",
simt = 1000)
```

**STEP 3.** Combine metrics from multiple outcomes

`<- rbind(nmaRandom, nmaCommon) dataMetrics `

**STEP 4.** Set data for rankinma As mentioned above,
users choose argument for parameter `metrics`

in the function
`GetMetrics()`

according to which global metric would be used
in analysis due to several rank metrics in network meta-analysis. The
following syntax is for users who would like to use SUCRA.

*Set data based on SUCRA*

```
<- (dataMetrics,
dataRankinma tx = tx,
outcome = outcome,
metrics = SUCRA,
metrics.name = "SUCRA")
```

*Set data based on P-score*

```
<- (dataMetrics,
dataRankinma tx = tx,
outcome = outcome,
metrics = P.score,
metrics.name = "P-score")
```

*Set data based on P-best*

```
<- (dataMetrics,
dataRankinma tx = tx,
outcome = outcome,
metrics = P.best,
metrics.name = "P-best")
```

**STEP 5.** Visualize global ranking metrics

*Illustrate beading plot based on SUCRA (without relative
effect)*

`PlotBeads(data = dataRankinma)`

*Illustrate beading plot sorted by P-score (with relative effects
and equidistant by rank)*

```
PlotBeads(data = dataRankinma,
scaleX = "Rank",
txtValue = "Effects")
```

*Illustrate colorblind-friendly beading plot based on
P-best*

```
PlotBeads(data = dataRankinma,
lgcBlind = TRUE)
```

*Illustrate spie plot based on P-score*

`PlotSpie(data = dataRankinma)`

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