netmeta (version 0.9-0)

netrank: Frequentist method to rank treatments in network

Description

Ranking treatments in frequentist network meta-analysis without resampling methods.

Usage

netrank(x, small.values="good")
"print"(x, sort=TRUE, digits=max(4, .Options$digits - 3), ...)

Arguments

x

An object of class netmeta (netrank function) or netrank (print function).

small.values

A character string specifying whether small treatment effects indicate a beneficial ("good") or harmful ("bad") effect, can be abbreviated.

sort

A logical indicating whether printout should be sorted by decreasing P-score.

digits

Minimal number of significant digits, see print.default.

...

Additional arguments passed on to print.data.frame function (used internally).

Value

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

Pscore

A named numeric vector with P-scores.

Pmatrix

Numeric matrix based on pairwise one-sided p-values.

small.values, x

As defined above.

version

Version of R package netmeta used to create object.

Details

Treatments are ranked based on a network meta-analysis. Ranking is performed by P-scores. P-scores are based solely on the point estimates and standard errors of the network estimates. They measure the extent of certainty that a treatment is better than another treatment, averaged over all competing treatments (Rücker and Schwarzer 2015).

The P-score of treatment i is defined as the mean of all 1 - P[j] where P[j] denotes the one-sided P-value of accepting the alternative hypothesis that treatment i is better than one of the competing treatments j. Thus, if treatment i is better than many other treatments, many of these P-values will be small and the P-score will be large. Vice versa, if treatment i is worse than most other treatments, the P-score is small.

The P-score of treatment i can be interpreted as the mean extent of certainty that treatment i is better than another treatment. This interpretation is comparable to that of the Surface Under the Cumulative RAnking curve (SUCRA) which is the rank of treatment i within the range of treatments, measured on a scale from 0 (worst) to 1 (best) (Salanti et al. 2011).

References

Rücker G & Schwarzer G (2015), Ranking treatments in frequentist network meta-analysis works without resampling methods. BMC Medical Research Methodology, 15, 58, DOI:10.1186/s12874-015-0060-8 .

Salanti G, Ades AE, Ioannidis JP (2011). Graphical methods and numerical summaries for presenting results from multiple-treatment meta-analysis: an overview and tutorial. Journal of Clinical Epidemiology, 64(2), 163--171.

See Also

netmeta

Examples

data(Senn2013)

net1 <- netmeta(TE, seTE, treat1, treat2, studlab,
                data=Senn2013, sm="MD")

nr1 <- netrank(net1)

nr1

print(nr1, sort=FALSE)