poth: Calculate precision of treatment hierarchy (POTH) metric

In poth: Precision of Treatment Hierarchy (POTH)

Calculate precision of treatment hierarchy (POTH) metric

Description

Precision of treatment hierarchy (POTH) is a metric to quantify the uncertainty in a treatment hierarchy in network meta-analysis

Usage

poth(x, se = NULL, small.values, pooled, trts = NULL)

## S3 method for class 'poth'
print(x, sort = TRUE, digits = 3, ...)

## S3 method for class 'poth'
summary(object, ...)

## S3 method for class 'summary.poth'
print(x, sort = TRUE, digits = 3, ...)

Arguments

x	Mandatory argument with suitable information on the treatment hierarchy (see Details).
se	Matrix of estimated standard errors for relative effects.
small.values	A character string specifying whether small outcome values indicate a beneficial ("desirable") or harmful ("undesirable") effect, can be abbreviated.
pooled	A character string indicating whether the treatment hierarchy is based on a common or random effects model. Either "common" or "random", can be abbreviated.
trts	An optional vector with treatment names. Must match the order of treatments provided for argument x.
sort	A logical indicating whether printout should be sorted by decreasing ranking metric.
digits	Minimal number of significant digits, see print.default.
...	Additional arguments (ignored).
object	An object of class summary.poth.

Details

This function calculates the precision of treatment hierarchy (POTH) metric to quantify the uncertainty in a treatment hierarchy in network meta-analysis (Wigle et al., 2024).

Argument x providing information on the treatment hierarchy is the only mandatory argument. The following input formats can be provided:

1. vector representing a ranking metric, i.e., SUCRAs or P-scores,

2. square matrix with the probabilities for each possible rank (with treatments in rows and ranks in columns),

3. MCMC samples (with samples in rows and treatments in columns),

4. relative effect matrix,

5. R object created with netmeta, netrank, or rankogram object from R package netmeta.

Argument se must be provided if argument x is a matrix with relative effects. Otherwise, argument se is ignored.

Argument small.values must be provided if argument x contains MCMC samples, relative effects, or is an object created with netmeta. This argument can be provided for an R object created with netrank or rankogram and is ignored otherwise.

For a netmeta, netrank, or rankogram object, argument pooled is by default equal to "random" if only the random effects model was considered in the analysis.

Argument trts is ignored for netmeta, netrank, and rankogram objects.

Value

An object of class poth with corresponding print function. The object is a list containing the following components:

poth	Separation in ranking metric.
ranking	A named numeric vector with rankings, i.e., SUCRAs or P-scores.
ranking.matrix	A square matrix with the probabilities for each possible rank (if information is available).
pooled	As defined above.

Author(s)

Augustine Wigle [e-mail], Guido Schwarzer [e-mail]

References

Wigle A, Béliveau A, Salanti G, Rücker G, Schwarzer G, Mavridis D, Nikolakopoulou A (2024): Precision of treatment hierarchy: A metric for quantifying uncertainty in treatment hierarchies in network meta-analysis. Preprint on arXiv, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.48550/arXiv.2501.11596")}

Examples

library("netmeta")
data(smokingcessation)
p1 <- pairwise(list(treat1, treat2, treat3),
  event = list(event1, event2, event3), n = list(n1, n2, n3),
  data = smokingcessation, sm = "OR")
net1 <- netmeta(p1, random = FALSE)

# Calculate probabilities for each possible rank
set.seed(1909) # make results reproducible
rg1 <- rankogram(net1)
rg1

# Calculate POTH
s1 <- poth(rg1)
s1

# Also print probabilities for each possible rank
summary(s1)

# Use SUCRAs to calculate POTH
nr1 <- netrank(rg1)
nr1
poth(nr1)
poth(nr1$ranking.common)

data(Senn2013)
net2 <- netmeta(TE, seTE, treat1.long, treat2.long, studlab,
                data = Senn2013, sm = "MD", random = FALSE)

# Use P-scores to calculate POTH
nr2 <- netrank(net2)
nr2
poth(nr2)

poth documentation built on June 8, 2025, 11:54 a.m.