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

Description

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

Usage

 1 2 3 4 5 6 7 8 9 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

Examples

 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 model <- mtc.model(smoking) # To save computation time we load the samples instead of running the model ## Not run: results <- mtc.run(model) results <- dget(system.file("extdata/luades-smoking.samples.gz", package="gemtc")) 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'

Example output  Rank probability; preferred direction = 1
[,1]    [,2]     [,3]     [,4]
A 0.00000 0.00250 0.105125 0.892375
B 0.05800 0.17600 0.661750 0.104250
C 0.22825 0.60075 0.170625 0.000375
D 0.71375 0.22075 0.062500 0.003000
A          B          C          D
0.03670833 0.39591667 0.68562500 0.88175000
2.5% 50% 97.5%
A    3   4     4
B    1   3     4
C    1   2     3
D    1   1     3

gemtc documentation built on May 15, 2021, 1:07 a.m.