Cost Effectiveness Statistic: A Proposal To Take Into Account The Patient Stratification Factors

TL;DR

This paper proposes a statistical method to refine cost-effectiveness analysis in clinical trials by accounting for patient stratification factors, improving the accuracy of incremental cost-effectiveness ratios.

Contribution

It introduces a cluster analysis-based approach to incorporate patient risk factors into economic evaluations, addressing confounding by indication.

Findings

Method effectively identifies patient groups with similar risk factors.

Reduces bias in cost-effectiveness comparisons across diverse patient populations.

Enhances decision-making in healthcare resource allocation.

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.