The mixed or heterogeneous multinomial logit (MIXL) model has become popular in a number of fields, especially marketing, health economics, and industrial organization. In most applications of the model, the vector of consumer utility weights on product attributes is assumed to have a multivariate normal (MVN) distribution in the population. Thus, some consumers care more about some attributes than others, and the IIA property of multinomial logit (MNL) is avoided (i.e., segments of consumers will tend to switch among the subset of brands that possess their most valued attributes). The MIXL model is also appealing because it is relatively easy to estimate. Recently, however, some researchers have argued that the MVN is a poor choice for modelling taste heterogeneity. They argue that much of the heterogeneity in attribute weights is accounted for by a pure scale effect (i.e., across consumers, all attribute weights are scaled up or down in tandem). This implies that choice behaviour is simply more random for some consumers than others (i.e., holding attribute coefficients fixed, the scale of their error term is greater). This leads to a “scale heterogeneity” MNL model (S-MNL). Here, we develop a generalized multinomial logit model (G-MNL) that nests S-MNL and MIXL. By estimating the S-MNL, MIXL, and G-MNL models on 10 data sets, we provide evidence on their relative performance. We find that models that account for scale heterogeneity (i.e., G-MNL or S-MNL) are preferred to MIXL by the Bayes and consistent Akaike information criteria in all 10 data sets. Accounting for scale heterogeneity enables one to account for “extreme” consumers who exhibit nearly lexicographic preferences, as well as consumers who exhibit very “random” behaviour (in a sense we formalize below).choice models, mixture models, consumer heterogeneity, choice experiments

We provide a user guide on the analysis of data (including best-worst and best-best data) generated from discrete choice experiments (DCEs), comprising a theoretical review of the main choice models followed by practical advice on estimation and post estimation. We also provide a review of standard software. In providing this guide we endeavor not only to provide guidance on choice modeling, but to do so in a way that provides researchers to the practicalities of data analysis. We argue that choice of modeling approach depends on: the research questions; study design and constraints in terms of quality/quantity of data and that decisions made in relation to analysis of choice data are often interdependent rather than sequential. Given the core theory and estimation of choice models is common across settings, we expect the theoretical and practical content of this paper to be useful not only to researchers within but also beyond health economics. Compliance with Ethical StandardsNo funding was received for the preparation of this paper. Emily Lancsar is funded by an ARC Fellowship (DE1411260). Emily Lancsar, Denzil Fiebig and Arne Risa Hole have no conflicts of interest. 2 Key Points for Decision Makers We provide a user guide on the analysis of data, including best-worst and best-best data, generated from DCEs, addressing the questions of DCE  We provide a theoretical overview of the main choice models and review three standard statistical software packages: Stata; Nlogit; and Biogeme.  Choice of modeling approach depends on the research questions; study design and constraints in terms of quality/quantity of data and decisions made in relation to analysis of choice data are often interdependent rather than sequential.  A health based DCE example for which we provide the data and estimation code is used throughout to demonstrate the data set up, variable coding, various model estimation and post estimation approaches.3

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Effective control of asthma requires regular preventive medication. Poor medication adherence suggests that patient preferences for medications may differ from the concerns of the prescribing clinicians. This study investigated patient preferences for preventive medications across symptom control, daily activities, medication side-effects, convenience and costs, using a discrete choice experiment embedded in a randomized clinical trial involving patients with mild-moderate persistent asthma. The present data were collected after patients had received 6 weeks' treatment with one of two drugs. Three choice options were presented, to continue with the current drug, to change to an alternative, hypothetical drug, or to take no preventive medication. Analysis used random parameter multinomial logit. Most respondents chose to continue with their current drug in most choice situations but this tendency differed depending on which medication they had been allocated. Respondents valued their ability to participate in usual daily activities and sport, preferred minimal symptoms, and were less likely to choose drugs with side-effects. Cost was also significant, but other convenience attributes were not. Demographic characteristics did not improve the model fit. This study illustrates how discrete choice experiments may be embedded in a clinical trial to provide insights into patient preferences.

Despite concerns about reporting biases and interpretation, self-assessed health (SAH) remains the measure of health most used by researchers, in part reflecting its ease of collection and in part the observed correlation between SAH and objective measures of health. Using a unique Australian data set, which consists of survey data linked to administrative individual medical records, we present empirical evidence demonstrating that SAH indeed predicts future health, as measured by hospitalizations, out-of-hospital medical services and prescription drugs. Our large sample size allows very disaggregate analysis and we find that SAH predicts more serious, chronic illnesses better than less serious illnesses. Finally we compare the predictive power of SAH relative to administrative data and an extensive set of selfreported health measures, SAH does not add to the predictive power of future utilization when the administrative data is included and improves prediction only marginally when the extensive survey-based health measures are included. Clearly there is value in the more extensive survey and administrative health data as well as greater cost of collection. Running title: Does self-assessed health measure health?

This paper estimates the impact of informal caregiving on self-reported well-being. It uses a sample of 23,285 respondents of the first eleven waves of the Household, Income and Labour Dynamics in Australia (HILDA).We apply a relatively new analytical method that enables us to estimate fixed effects ordered logit to analyse subjective well-being. The econometric estimates show that providing informal care has a negative effect on subjective well-being.The empirical evidence of our paper could be helpful to inform policy makers to better understand the impact of caregiving and design the appropriate long term care policies and support services.