gemtc (version 1.0-1)

rank.probability: 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 mtc.rank.probability
print(x, ...)
# S3 method for mtc.rank.probability
plot(x, ...)
sucra(ranks)
rank.quantiles(ranks, probs=c("2.5%"=0.025, "50%"=0.5, "97.5%"=0.975))

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).

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.

Author

Gert van Valkenhoef, Joël Kuiper

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.

See Also

relative.effect

Examples

model <- mtc.model(smoking)
# To save computation time we load the samples instead of running the model
if (FALSE) 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'