Explore preference heterogeneity for treatment among people with Type 2 diabetes: A comparison of random-parameters and latent-class estimation techniques

Abstract

There has been an increasing interest in studying patient preference heterogeneity to support regulatory decision-making. While the traditional mixed logit (MXL) and the latent class logit (LCL) models have been commonly used to analyze preference heterogeneity in discrete choice data, they have limitations. This study empirically compares a random effects latent class logit (RELCL) model to the traditional approaches using preference data from a discrete-choice experiment among patients with Type 2 diabetes. Each survey contained 18 pairs of hypothetical diabetes medications that differed in six attributes. Sensitivity analysis is also performed to explore under what circumstances RELCL outperforms LCL.Significant preference heterogeneity was found in all models. The 2-class RELCL has the lowest BIC (8350.64) and prediction error (11.6%) compared to MXL (BIC = 9345.40; pred. err. = 13.0%) and the 5-class LCL (BIC = 8440.30; pred. err. = 16.4%), indicating improved model fit. Allowing random effects also reduces the number of classes from five in LCL to two, both having significant policy and clinical implications. RELCL provides the flexibility of LCL and the parsimony of MXL. Both our empirical results and sensitivity analysis shows that when there is significant preference heterogeneity among patients that cannot be captured by a small number of clusters, RELCL may be used to generate more accurate predictions and more parsimonious results that are policy-relevant.