A discrete mixture decision field theory model for capturing preference and decision process heterogeneity in health choices.

Discrete choice experiments in healthcare typically include only a small number of choice tasks for each participant to minimise the burden on responders. The complexity of healthcare issues means that choice tasks often include many attributes and/or alternatives. Consequently, individual-level models are not possible. Differences in behaviour, preferences, and decision-making processes must be captured with the use of more complex behavioural models. The inclusion of sociodemographic parameters in the model aids the detection of deterministic heterogeneity, while stochastic heterogeneity is typically captured using random parameters or latent class structures. However, neither of these methods are particularly well-suited to disentangling confounding sources of heterogeneity, that is, to detect individuals who make decisions in different ways separately from the identification of different preferences. In the current work, we address this issue using a discrete mixture model. This model estimates probabilities for each individual having particular tastes, and separately estimates probabilities of using different decision-making processes. In particular, we develop a discrete mixture decision field theory (DMDFT) model to capture preference heterogeneity through different attribute importance parameters whilst accommodating the different processing speeds of decision-makers and propensities to be subject to the similarity effect through different process parameters. We apply the model to datasets from discrete choice experiments on tobacco preferences. We demonstrate that (a) DMDFT models outperform latent class DFTs, providing clearer insights on individual differences (b) the models outperform their counterpart econometric choice models, and (c) the models find clear differences in the individuals’ decision-making processes as well as preferences.