Simulated Annealing for Model-Robust Partial Profile Choice Designs in Healthcare Preference Studies

TL;DR

This paper introduces a simulated annealing algorithm to create model-robust partial profile choice designs for healthcare preference studies, effectively estimating interaction effects despite unknown interactions.

Contribution

It develops a novel SA-based method for constructing partial profile designs that are robust to unknown interaction effects in healthcare DCEs.

Findings

The SA design performs well across various interaction-effect scenarios.

Simulation experiments show improved estimation of interaction effects.

Real case study confirms practical applicability of the proposed method.

Abstract

Discrete Choice Experiments (DCEs) investigate participants' preferences by observing their choice behavior in hypothetical scenarios and are widely used in the domain of healthcare. To reduce participants' cognitive burden, especially when dealing with a large number of attributes, researchers often employ partial profile designs. In these designs, certain attributes within each choice set are kept constant. Current literature on partial profile designs mainly focuses on main-effects models rather than interaction-effect models, with certain partial profile designs even incapable of estimating interaction effects. To address this issue, this paper introduces an Simulated Annealing (SA) algorithm to construct partial profile designs based on an interaction-effects model. During the experimental design phase, the existence and magnitude of interaction effects are often unknown. Therefore, this paper proposes a model-robust experimental design strategy. Through extensive simulation experiments and a real-life case study, we demonstrate that our SA model-robust partial profile design performs relatively well regardless of the underlying model.