A Novel Bayesian Extrapolation Design for Assessing Equivalence in Exposure-Response Curves between Pediatric and Adult Populations

TL;DR

This paper introduces a Bayesian extrapolation method that assesses the entire exposure-response curve similarity between adult and pediatric populations, improving upon existing point-based comparison techniques.

Contribution

It presents a novel Bayesian approach for comprehensive E-R curve comparison, including sample size determination and error control, tailored for pediatric extrapolation.

Findings

Method maintains stable type I and II error rates.

Simulation studies validate the approach's effectiveness.

Incorporates Bayesian and frequentist principles for robust analysis.

Abstract

Development of effective treatments in pediatric population poses unique scientific and ethical challenges in addition to the small population. In this regard, both the U.S. and E.U. regulations suggest a complementary strategy, pediatric extrapolation, based on assessing the relevance of existing information in the adult population to the pediatric population. The pediatric extrapolation approach often relies on data extrapolation from adults, contingent upon evidence of similar disease progression, pharmacology and clinical response to treatment between adult and children. Similarity evaluation in pharmacology is usually characterized through the exposure-response relationship. Current methodologies for comparing exposure-response (E-R) curves between these groups are inadequate, typically focusing on isolated data points rather than the entire curve spectrum (Zhang et al., 2021). To overcome this limitation, we introduce an innovative Bayesian approach for a comprehensive evaluation of E-R curve similarities between adult and pediatric populations. This method encompasses the entire curve, employing logistic regression for binary endpoints. We have developed an algorithm to determine sample size and key design parameters, such as the Bayesian posterior probability threshold, and utilize the maximum curve distance as a measure of similarity. Integrating Bayesian and frequentist principles, our approach involves developing a method to simulate datasets under both null and alternative hypotheses, allowing for type I error and type II error control. Simulation studies and sensitivity analyses demonstrate that our method maintains a stable performance with type I error and type II error control.