fitted.mtrank {mtrank} | R Documentation |
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 class 'mtrank'
fitted(object, treat1, treat2, type, ...)
## S3 method for class 'fitted.mtrank'
print(x, type = attr(x, "type"), digits = 4, ...)
Arguments
object |
An object of class |
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 |
x |
An object of class |
digits |
Minimal number of significant digits for proportions,
see |
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 thantreat2
,"tie": probability that
treat1
is equal totreat2
,"worse": probability that
treat1
is worse thantreat2
,"all": all three probabilities.
Please note that all the arguments of this function are mandatory.
Value
The probability (or probabilities) of interest for the comparison
treat1
vs treat2
based on the argument type
.
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"))
## Not run:
# 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"))
## End(Not run)