netmeta (version 0.8-0)

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")
## S3 method for class 'netrank':
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 c("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. Submitted.

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)