Model Selection versus Model Averaging in Dose Finding Studies

TL;DR

This paper compares model selection and model averaging methods for dose response modeling in Phase II clinical trials, analyzing their theoretical properties and performance through simulations and real data application.

Contribution

It provides a comprehensive comparison of model selection and averaging techniques, including asymptotic analysis and simulation results, for dose finding in clinical studies.

Findings

Model averaging often yields more robust dose estimates.

Selection criteria performance varies with sample size and effect size.

Simulation results highlight trade-offs between methods.

Abstract

Phase II dose finding studies in clinical drug development are typically conducted to adequately characterize the dose response relationship of a new drug. An important decision is then on the choice of a suitable dose response function to support dose selection for the subsequent Phase III studies. In this paper we compare different approaches for model selection and model averaging using mathematical properties as well as simulations. Accordingly, we review and illustrate asymptotic properties of model selection criteria and investigate their behavior when changing the sample size but keeping the effect size constant. In a large scale simulation study we investigate how the various approaches perform in realistically chosen settings. Finally, the different methods are illustrated with a recently conducted Phase II dosefinding study in patients with chronic obstructive pulmonary disease.