ZIKQ: An innovative centile chart method for utilizing natural history data in rare disease clinical development

TL;DR

This paper introduces ZIKQ, a new method for creating reference centile charts from censored natural history data, aiding rare disease clinical trials by providing age-dependent benchmarks.

Contribution

The paper presents a novel calibrated zero-inflated kernel quantile (ZIKQ) method for constructing centile charts from censored natural history data in rare diseases.

Findings

ZIKQ effectively handles censored data and zero-inflation.

Application to Duchenne Muscular Dystrophy demonstrates utility.

Facilitates treatment evaluation and patient enrollment.

Abstract

Utilizing natural history data as external control plays an important role in the clinical development of rare diseases, since placebo groups in double-blind randomization trials may not be available due to ethical reasons and low disease prevalence. This article proposed an innovative approach for utilizing natural history data to support rare disease clinical development by constructing reference centile charts. Due to the deterioration nature of certain rare diseases, the distributions of clinical endpoints can be age-dependent and have an absorbing state of zero, which can result in censored natural history data. Existing methods of reference centile charts can not be directly used in the censored natural history data. Therefore, we propose a new calibrated zero-inflated kernel quantile (ZIKQ) estimation to construct reference centile charts from censored natural history data. Using the application to Duchenne Muscular Dystrophy drug development, we demonstrate that the reference centile charts using the ZIKQ method can be implemented to evaluate treatment efficacy and facilitate a more targeted patient enrollment in rare disease clinical development.