Adaptive Endpoints Selection with Application in Rare Disease

Abstract

In rare diseases, there are many unanswered questions that are critical to clinical development. Among them, one important question is how to choose primary endpoints that translate into meaningful improvement of health outcomes for patients while maximizing trial probability of success at the same time. A natural history study is often recommended by regulatory agencies. Following this traditional approach has dampened enthusiasm for many drug developers because it entails much higher cost and longer timeline. We propose to use an innovative design that allows adaptation on primary endpoint(s) so that learning about disease endpoints can be done within the pivotal trial itself through a subset of patients (i.e., informational cohort). The overall Family Wise Error Rate (FWER) will be controlled through the use of combination test following partition testing principle. A case example in patients with Pompe disease is used to show that the proposed innovative design maintains robust power across treatment effect scenarios while traditional fixed design bears the high risk of failure due to incorrect endpoint selection. Even if multiple endpoints can be included as primary, the proposed innovative design can still improve power over traditional designs by optimizing alpha allocations in cases with differential treatment effects.

KEYWORDS:

• Combination function
• Endpoint selection
• Family-wise error
• Informational design
• Rare diseases
• Simulation comparison

Supplementary Materials

The authors provide the R code files as supplementary materials for generating the following results in the paper: Table 2, Table 3, Table 4, and Table 5.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.