Hierarchical bayesian model accounts for heterogeneity in oncologists' stated preference on various breast cancer treatments

Abstract

Objectives: Traditional stated-preference models with fixed effects assume that individuals behave similarly. However, empirical evidence has shown that individuals’ preferences are often diverse. Hierarchical Bayesian models that include random effects provide individual-specific utilities to account for heterogeneity. This research studies oncologists’ choices about various pharmaceutical therapies for patients who have metastatic breast cancer. Methods: In this discrete choice experiment conducted in Lima, Peru, each of 113 oncologists was presented with 11 choice tasks (each consisting of four scenarios of therapies plus the NONE option) and asked to pick the best choice. The attributes included Treatment Scheme, Patient Recovery Status, Treatment Length, Cost, and Risk Factors. Hierarchical Bayesian methods were used in this multinomial logit conjoint analysis to account for heterogeneity in preferences.