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help for clusterank command

Title

Clustering for treatments of a network of interventions according to their performance on two outcomes

Description

When performing a network meta-analysis the competing treatments can be ranked according to their performance on one or more outcomes (e.g. effectiveness and safety).
However, the relative ranking for each outcome might be different and this makes the choice of the 'best' treatment challenging.
A possible way to make inferences based on results for two outcomes is by using a two-dimensional plot and constructing groups of treatments with similar performance on both outcomes (Chaimani, 2013).
To form meaningful groups of treatments, hierarchical clustering methods have been employed.

The clusterank command performs hierarchical cluster analysis to group the competing treatments in meaningful groups.
It requires the values of a ranking measure (e.g. SUCRA percentages or MDS dimension; see sucra, mdsrank) for two outcomes.

Syntax

clusterank
varlist
[,
best(min|max)
method(linkage_method distance_metric)
clusters(intger)
scatteroptions(string)
denrogram

The clusterank command requires in varlist to specify the following variables:

outcome1 and outcome2 the estimated values of a ranking measure for two different outcomes

Optionally we can additionally specify the variable t (numerical or string) the names pf the competinf treatments in the network

Note: if the variable t has not been specified, the treatments are named according to the observation numbers.

Options

best(min|max) specifies whether larger or smaller values of the ranking measure correspond to better outcome with the treatment (applies to both outcomes).
The default option is max.

method(linkage_method distance_metric) specifies which linkage method and distance metric are used (see cluster). By default, the method of hierarchical clustring
is decided according to the cophenetic correlation coefficient (Handl, 2005).

clusters(intger) specifies the number of clusters used to group the treatments. By default, the optimal number of clusters is decided according to the
'clustering gain' (Jung, 2003).

scatteroptions(string) specifies standard options of scatter.

dendrogram specifies that the dendrogram of the hierarchical analysis is displayed instead of the clustered ranking plot.

Examples

. clusterank outcome1 outcome2 treat, method(singlelinkage Euclidean) clusters(5)

. clusterank outcome1 outcome2, scatter(msymbol(square))

. clusterank outcome1 outcome2 t, dendrogram

Details

The command chooses the appropriate metric (Euclidean, squared Euclidean, absolute-value distance, etc.) and linkage method (single, average, weighted, complete, ward, centroid, median)
based on the cophenetic correlation coefficient, which measures how faithfully the output dendrogram represents the dissimilarities between observations (Handl, 2005).
The optimal level of dendrogram and the optimal number of clusters are chosen using an internal cluster validation measure, called 'clustering gain' (Jung, 2003).
This measure has been designed to have a maximum value when intra-cluster similarity is maximized and inter-cluster similarity is minimized.

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