  The solution here proposed can be used to conduct economic analysis in
randomized clinical trials. It is based on a statistical approach and aims at
calculating a revised version of the incremental costeffective ratio (ICER) in
order to take into account the key factors that can influence the choice of
therapy causing confounding by indication. Let us take as an example a new
therapy to treat cancer being compared to an existing therapy with
effectiveness taken as time to death. A challenging problem is that the ICER is
defined in terms of means over the entire treatment groups. It makes no
provision for stratification by groups of patients with differing risk of
death. For example, for a fair and unbiased analysis, one would desire to
compare time to death in groups with similar life expectancy which would be
impacted by factors such as age, gender, disease severity, etc. The method we
decided to apply is borrowed by cluster analysis and aims at (i) discard any
outliers in the set under analysis that may arise, (ii) identify groups (i.e.
clusters) of patients with "similar" key factors.
