mtrank (version 0.1-1)

paired_pref: Calculate pairwise probabilities for mtrank object

Description

This function uses the estimates of ability and tie prevalence parameters from a mtrank object and calculates pairwise probabilities about the preference or the tie between two treatments based on equations (7) and (8) in Evrenoglou et al. (2024).

Usage

paired_pref(x, treat1, treat2, type)
# S3 method for paired_pref
print(x, type = attr(x, "type"), digits = 4, ...)

Value

The probability (or probabilities) of interest for the comparison treat1 vs treat2 based on the argument type.

Arguments

x

An object of class mtrank.

treat1

The first treatment considered in the treatment comparison.

treat2

The second treatment considered in the treatment comparison.

type

A character vector specifying the probability of interest. Either "better", "tie", "worse", or "all" (can be abbreviated).

digits

Minimal number of significant digits for proportions, see print.default.

...

Additional arguments (passed on to prmatrix).

Details

Pairwise probabilities between any two treatments in the network can be calculated using the ability estimates obtained from mtrank and equations (7) and (8) in Evrenoglou et al. (2024). The probabilities are calculated in the direction treat1 vs treat2. The available probability types are

• "better": probability that treat1 is better than treat2,

• "tie": probability that treat1 is equal to treat2,

• "worse": probability that treat1 is worse than treat2,

• "all": all three probabilities.

Please note that all the arguments of this function are mandatory.

References

Evrenoglou T, Nikolakopoulou A, Schwarzer G, Rücker G, Chaimani A (2024): Producing treatment hierarchies in network meta-analysis using probabilistic models and treatment-choice criteria. https://arxiv.org/abs/2406.10612

Examples

data(antidepressants)
#
ranks <- tcc(treat = drug_name, studlab = studyid,
  event = responders, n = ntotal, data = antidepressants,
  mcid = 1.25, sm = "OR", small.values = "undesirable")
#
fit <- mtrank(ranks)
#
paired_pref(fit, type = c("better", "worse"),
  treat1 = "bupropion", treat2 = "escitalopram")
#
paired_pref(fit, type = c("better", "worse"),
  treat1 = "escitalopram", treat2 = "bupropion")
#
paired_pref(fit, type = "all",
  treat1 = c("bupropion", "escitalopram"),
  treat2 = c("escitalopram", "bupropion"))