Assessing the similarity of dose response and target doses in two non-overlapping subgroups

TL;DR

This paper develops statistical methods to assess the similarity of dose-response curves and target doses between two non-overlapping patient subgroups, using confidence intervals and validated through simulations and a real case study.

Contribution

It introduces new confidence interval-based techniques for evaluating dose-response and target dose similarity in non-overlapping subgroups, addressing a gap in clinical dose finding studies.

Findings

Confidence intervals effectively determine dose-response similarity.

Methods demonstrate good coverage probabilities and controlled error rates.

Application to a real case study illustrates practical utility.

Abstract

We consider two problems that are attracting increasing attention in clinical dose finding studies. First, we assess the similarity of two non-linear regression models for two non-overlapping subgroups of patients over a restricted covariate space. To this end, we derive a confidence interval for the maximum difference between the two given models. If this confidence interval excludes the equivalence margins, similarity of dose response can be claimed. Second, we address the problem of demonstrating the similarity of two target doses for two non-overlapping subgroups, using again a confidence interval based approach. We illustrate the proposed methods with a real case study and investigate their operating characteristics (coverage probabilities, Type I error rates, power) via simulation.