rankinma_vignette

Brief overview

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)

Feature

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.

Flow and functions

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

Situation 1: Users have treatment ranking metrics of outcome(s).

Situation 2: Users have data for network meta-analysis of a single outcome but do not get treatment ranking metrics yet.

Situation 3: Users have data for network meta-analysis of various outcomes but do not get treatment ranking metrics yet.

Usage and examples

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 bar chart when users already have treatment ranking metrics (e.g. P-score).

STEP 1. Build data

data <- data.frame(tx = c("A", "B", "C", "A", "B", "C"),
                   outcome = c("mortality", "mortality", "mortality", "recurrent", "recurrent", "recurrent"), 
                   SUCRA = c(0.8, 0.7, 0.5, 0.9, 0.5, 0.8))

STEP 2. Set data for rankinma

dataRankinma <- SetMetrics(data, 
                           tx = tx, 
                           outcome = outcome, 
                           metrics = SUCRA, 
                           metrics.name = "SUCRA")

STEP 3. Illustrate bar chart

PlotBar(data = dataRankinma)

Output:

Figure 1. examples of bar chart for SUCRA on two outcomes.

Example 2 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)
nmaOutput <- netmeta(TE, 
                     seTE, 
                     treat1, 
                     treat2, 
                     studlab, 
                     data = Senn2013, 
                     sm = "SMD")

STEP 2. Get Probabilities

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

STEP 3. Set data for rankinma

dataRankinma <- SetMetrics(dataMetrics, 
                           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 6.2-1).
#> 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 2.6-0).
#> 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

Figure 2A. an exmaple of composite line chart for probabilities of treatments on each rank.

or

Figure 2B. an example of accumulative bar chart for probabilities of treatments on each rank.

Example 3 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)
nmaOutput <- netmeta(TE, 
                     seTE, 
                     treat1, 
                     treat2, 
                     studlab, 
                     data = Senn2013, 
                     sm = "SMD")

STEP 2. Get SUCRA

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

STEP 3. Combine metrics from multiple outcomes

dataMetrics <- rbind(nmaRandom, nmaCommon)

STEP 4. Set data for rankinma

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

STEP 5. Illustrate beading plot

PlotBeads(data = dataRankinma)

Output:

#> Check variables:
#>  Inherit -------------------------------------------------- V
#>  Outcome -------------------------------------------------- V
#>  Prefer  -------------------------------------------------- V
#>  Metrics -------------------------------------------------- V
#>  Model   -------------------------------------------------- V
#> 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
#> Check variables:
#>  Inherit -------------------------------------------------- V
#>  Outcome -------------------------------------------------- V
#>  Prefer  -------------------------------------------------- V
#>  Metrics -------------------------------------------------- V
#>  Model   -------------------------------------------------- V
#> 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
#> Check variables:
#>  Treatment -------------------------------------------------- V
#>  Outcome   -------------------------------------------------- V
#>  Metrics name  ---------------------------------------------- V
#>  Metrics   -------------------------------------------------- V
#>  Transparency   --------------------------------------------- V
#> 
#> 
#> 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
#>  Inherit -------------------------------------------------- V
#>  Metrics -------------------------------------------------- V
#>  Color ---------------------------------------------------- V

Figure 3. an example of beading plot for P-score on two outcomes