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 and P-score) 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(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.Illustrate line chart`PlotLine(data = dataRankinma, compo = TRUE)`

Output:

`#> Loading required package: meta #> Loading 'meta' package (version 6.5-0). #> Type 'help(meta)' for a brief overview. #> Readers of 'Meta-Analysis with R (Use R!)' should install #> older version of 'meta' package: https://tinyurl.com/dt4y5drs #> Loading 'netmeta' package (version 2.8-2). #> Type 'help("netmeta-package")' for a brief overview. #> Readers of 'Meta-Analysis with R (Use R!)' should install #> older version of 'netmeta' package: https://tinyurl.com/kyz6wjbb`

or

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

STEP 2.Get SUCRA`<- 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)`

STEP 3.Combine metrics from multiple outcomes`<- rbind(nmaRandom, nmaCommon) dataMetrics`

STEP 4.Set data for rankinma`<- (dataMetrics, dataRankinma tx = tx, outcome = outcome, metrics = P.score, metrics.name = "P-score")`

STEP 5.Illustrate beading plot`PlotBeads(data = dataRankinma)`

Output:

`#> Summary of metrics: #> Metrics: P-score #> Outcomes: 1 #> Treatments: 10 #> #> List of treatments: #> 1 acar #> 2 benf #> 3 metf #> 4 migl #> 5 piog #> 6 plac #> 7 rosi #> 8 sita #> #> 9 sulf #> 10 vild #> Summary of metrics: #> Metrics: P-score #> Outcomes: 1 #> Treatments: 10 #> #> List of treatments: #> 1 acar #> 2 benf #> 3 metf #> 4 migl #> 5 piog #> 6 plac #> 7 rosi #> 8 sita #> #> 9 sulf #> 10 vild #> #> #> Summary of metrics: #> Metrics: P-score #> Outcomes: 2 #> Treatments: 10 #> #> List of outcomes: #> 1 HbA1c.random #> 2 HbA1c.common #> List of treatments: #> 1 acar #> 2 benf #> 3 metf #> 4 migl #> 5 piog #> 6 plac #> 7 rosi #> 8 sita #> #> 9 sulf #> 10 vild`

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Daly, C. H., Mbuagbaw, L., Thabane, L., Straus, S. E., & Hamid,
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