nmaleague {NMA}

Generating a league table

Description

A league table is generated for all possible pairs of the treatments. The league table can be outputted as a CSV file through setting out.csv="filename".

Usage

nmaleague(x, method="NH", eform=FALSE, digits=3, PI=FALSE, out.csv=NULL)

Arguments

x

Output object of setup

method

Estimation and prediction method. NH: Noma-Hamura's improved REML-based methods (default). REML: The ordinary REML method.

eform

A logical value that specify whether the outcome should be transformed by exponential function (default: FALSE)

digits

Number of decimal places

PI

A logical value that specify whether the inference or prediction results are provided

out.csv

A character object that specify a filename if the user wants to output the league table as a CSV file (e.g., out.csv="out_league.csv").

Value

A league table is generated.

References

Nikolakopoulou, A., White, I. R., and Salanti, G. (2021). Network meta-analysis. In: Schmid, C. H., Stijnen, T., White, I. R., eds. Handbook of Meta-Analysis. CRC Press; pp. 187-217.

Noma, H., Hamura, Y., Gosho, M., and Furukawa, T. A. (2023). Kenward-Roger-type corrections for inference methods of network meta-analysis and meta-regression. Research Synthesis Methods 14, 731-741.

Noma, H., Hamura, Y., Sugasawa, S., and Furukawa, T. A. (2023). Improved methods to construct prediction intervals for network meta-analysis. Research Synthesis Methods 14, 794-806.

White, I. R., Barrett, J. K., Jackson, D., and Higgins, J. P. (2012). Consistency and inconsistency in network meta-analysis: model estimation using multivariate meta-regression. Research Synthesis Methods 3, 111-125.

Examples

data(smoking)

smk2 <- setup(study=study,trt=trt,d=d,n=n,measure="OR",ref="A",data=smoking)

nmaleague(smk2)                                        # default setting
nmaleague(smk2, eform=TRUE)                            # transformed to exponential-scale
nmaleague(smk2, eform=TRUE, digits=2)                  # digits can be changed
nmaleague(smk2, eform=TRUE, PI=TRUE)                   # prediction intervals