There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.
Abstract
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.