rank.probability: Calculating rank-probabilities In gemtc: Network Meta-Analysis Using Bayesian Methods

 rank.probability R Documentation

Calculating rank-probabilities

Description

Rank probabilities indicate the probability for each treatment to be best, second best, etc.

Usage

``````rank.probability(result, preferredDirection=1, covariate=NA)

## S3 method for class 'mtc.rank.probability'
print(x, ...)
## S3 method for class 'mtc.rank.probability'
plot(x, ...)

sucra(ranks)
rank.quantiles(ranks, probs=c("2.5%"=0.025, "50%"=0.5, "97.5%"=0.975))
``````

Arguments

 `result` Object of S3 class `mtc.result` to be used in creation of the rank probability table `preferredDirection` Preferential direction of the outcome. Set 1 if higher values are preferred, -1 if lower values are preferred. `covariate` (Regression analyses only) Value of the covariate at which to compute rank probabilities. `x` An object of S3 class `rank.probability`. `...` Additional arguments. `ranks` A ranking matrix where the treatments are the rows (e.g. the result of rank.probability). `probs` Probabilities at which to give quantiles.

Details

For each MCMC iteration, the treatments are ranked by their effect relative to an arbitrary baseline. A frequency table is constructed from these rankings and normalized by the number of iterations to give the rank probabilities.

Value

`rank.probability`: A matrix (with class `mtc.rank.probability`) with the treatments as rows and the ranks as columns. `sucra`: A vector of SUCRA values. `rank.quantiles`: A matrix with treatments as rows and quantiles as columns, giving the quantile ranks (by default, the median and 2.5% and 97.5% ranks).

Author(s)

Gert van Valkenhoef, Joël Kuiper

`relative.effect`

Examples

``````model <- mtc.model(smoking)
# To save computation time we load the samples instead of running the model
## Not run: results <- mtc.run(model)

ranks <- rank.probability(results)
print(ranks)
## Rank probability; preferred direction = 1
##       [,1]     [,2]     [,3]     [,4]
## A 0.000000 0.003000 0.105125 0.891875
## B 0.057875 0.175875 0.661500 0.104750
## C 0.228250 0.600500 0.170875 0.000375
## D 0.713875 0.220625 0.062500 0.003000

print(sucra(ranks))
##          A          B          C          D
## 0.03670833 0.39591667 0.68562500 0.88175000

print(rank.quantiles(ranks))
##   2.5% 50% 97.5%
## A    3   4     4
## B    1   3     4
## C    1   2     3
## D    1   1     3

plot(ranks) # plot a cumulative rank plot
plot(ranks, beside=TRUE) # plot a 'rankogram'
``````

gemtc documentation built on July 9, 2023, 5:33 p.m.