Clinical research and methodology What usage and what hierarchical order for secondary endpoints?

TL;DR

This paper discusses methods for analyzing secondary endpoints in clinical trials, focusing on hierarchical testing procedures to control error rates and accurately identify treatment effects.

Contribution

It compares hierarchical testing with co-primary endpoint approaches, highlighting the advantages of hierarchical methods in clinical research analysis.

Findings

Hierarchical testing effectively controls type I error in secondary endpoint analysis.

Hierarchical approach helps identify treatment effects beyond primary endpoints.

Method simplifies the interpretation of secondary endpoint results.

Abstract

In a randomised clinical trial, when the result of the primary endpoint shows a significant benefit, the secondary endpoints are scrutinised to identify additional effects of the treatment. However, this approach entails a risk of concluding that there is a benefit for one of these endpoints when such benefit does not exist (inflation of type I error risk). There are mainly two methods used to control the risk of drawing erroneous conclusions for secondary endpoints. The first method consists of distributing the risk over several co-primary endpoints, so as to maintain an overall risk of 5%. The second is the hierarchical test procedure, which consists of first establishing a hierarchy of the endpoints, then evaluating each endpoint in succession according to this hierarchy while the endpoints continue to show statistical significance. This simple method makes it possible to show the additional advantages of treatments and to identify the factors that differentiate them.