mtrank (version 0.2-0)

fitted.mtrank: Calculate pairwise fitted probabilities for mtrank object.

Description

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

Usage

# S3 method for mtrank
fitted(object, treat1, treat2, type, ...)
# S3 method for fitted.mtrank
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

object

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).

...

Additional arguments (passed on to prmatrix).

x

An object of class fitted.mtrank.

digits

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

Details

Pairwise fitted 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 fitted 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)
#
pw1 <- pairwise(studlab = studyid, treat = drug_name,
  n = ntotal, event = responders,
  data = antidepressants, sm = "OR")
# Use subset to reduce runtime
pw0 <- subset(pw1, studyid < 60)
#
net0 <- netmeta(pw0, reference.group = "tra")
#
ranks0 <- tcc(net0, swd = 1.20, small.values = "undesirable")
#
fit0 <- mtrank(ranks0)
#
fitted(fit0, type = c("better", "worse"),
  treat1 = "bupropion", treat2 = "escitalopram")
#
fitted(fit0, type = c("better", "worse"),
  treat1 = "escitalopram", treat2 = "bupropion")
#
fitted(fit0, type = "all",
  treat1 = c("bupropion", "escitalopram"),
  treat2 = c("escitalopram", "bupropion"))

if (FALSE) {
# Run analysis with full data set
net1 <- netmeta(pw1, reference.group = "tra")
#
ranks1 <- tcc(net1, swd = 1.20, small.values = "undesirable")
#
fit1 <- mtrank(ranks1)
#
fitted(fit1, type = c("better", "worse"),
  treat1 = "bupropion", treat2 = "escitalopram")
#
fitted(fit1, type = c("better", "worse"),
  treat1 = "escitalopram", treat2 = "bupropion")
#
fitted(fit1, type = "all",
  treat1 = c("bupropion", "escitalopram"),
  treat2 = c("escitalopram", "bupropion"))
}