*! version 1.1 27may2015

Title

network rank -- Rank treatments after network meta-analysis

Syntax

network rank min|max [if] [in] [, mvmeta_pbest_options]

network rank makes sensible choices for
if and in which it would be unwise to change.

mvmeta_pbest_options are any suboptions for the pbest option of mvmeta.
network rank makes sensible choices for
zero and id() which it would be unwise to change.
The options listed below are likely to be useful.

mvmeta_pbest_options
all Reports probabilities for all ranks.
The default is to report only the probabilities of being the best treatment.

reps(#) Set the number of replicates - larger numbers reduce Monte Carlo error

seed(#) Set the random number seed for reproducibility

bar Draw a bar graph of ranks

line Draw a line graph of ranks

cumulative Make the bar or line graph show cumulative ranks

predict Ranks the true effects in a future study with the same covariates,
thus allowing for heterogeneity as well as parameter uncertainty,
as in the calculation of prediction intervals (Higgins et al, 2009).
The default behaviour is instead to rank linear predictors and does not allow for heterogeneity.

meanrank Tabulate the mean rank and the SUCRA (Salanti et al, 2011).
The SUCRA is the rescaled mean rank: it
is 1 when a treatment is certain to be the best and 0 when a treatment is certain to be the worst.

saving(filename) Writes the draws from the posterior distribution
(indexed by the identifier and the replication number) to filename.

replace Allows filename in saving(filename) to be overwritten.

clear Loads the rank data into memory and gives the commands needed to reproduce the table and graph.

Other options
trtcodes makes the display use the treatment codes rather than the treatment names.

Description

network rank ranks treatments after a network meta-analysis has been fitted.
This currently only works after running network meta consistency or network meta inconsistency
in the augmented format.

Use network rank min if the best treatment is that with the lowest (most negative) treatment
effect
and network rank max if the best treatment is that with the highest (most positive) treatment
effect.

After fitting the inconsistency model, and after fitting a meta-regression,
the treatment effect - and hence the ranking - differs across studies.
network rank therefore computes and displays ranks for all studies unless if or in is specified.
After fitting the consistency model without covariates, on the other hand,
the ranks are the same for all studies
and network rank displays ranks for just the first study.

Examples

Assume the thrombolytics data have been loaded and the consistency model has been fitted.

Find the probabilities that each treatment is the best
(i.e. lowest log hazard ratio)
under the consistency model:

. network rank min

The same, setting the seed (for reproducibility);
considering all ranks, not just the best; drawing a rankogram;
and increasing the number of replicates (to reduce Monte Carlo error):

. network rank min, seed(48106) all line cumul reps(10000) meanrank

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