Two-dose vs. Three-Dose Optimization Under Sample Size Constraint

TL;DR

This paper compares two-dose and three-dose optimization strategies in early-phase oncology drug development, demonstrating that three doses are generally preferable unless strong evidence suggests dropping one, supported by mathematical and simulation analyses.

Contribution

It introduces a mathematical approximation and simulation approach to evaluate the benefits of three doses over two under fixed sample sizes in dose optimization.

Findings

Three doses are generally more advantageous than two for optimization.

Mathematical approximation effectively guides dose selection decisions.

Simulation results support the theoretical findings across various scenarios.

Abstract

Dose optimization is a hallmark of Project Optimus for oncology drug development. The number of doses to include in a dose optimization study depends on the totality of evidence, which is often unclear in early-phase development. With equal sample sizes per dose, carrying three doses is clearly more advantageous than two for optimization. In this paper, we show that, even when the total sample size is fixed, it is still preferable to carry three unless there is very strong evidence that one can be dropped. A mathematical approximation is applied to guide the investigation, followed by a simulation study to complement the theoretical findings. Semi-quantitative guidance is provided for practitioners, addressing both randomized and non-randomized dose optimization while considering population homogeneity.