Use of Discrete Choice Experiments in health economics: An update of the literature

The vast majority of stated preference research in health economics has been conducted in the random utility model paradigm using discrete choice experiments (DCEs). Ryan and Gerard (2003) have reviewed the applications of DCEs in the field of health economics. We have updated this initial work to include studies published between 2001 and 2007. Following the methods of Ryan and Gerard, we assess the later body of work, with respect to the key characteristics of DCEs such as selection of attributes and levels, experimental design, preference measurement, estimation procedure and validity. Comparisons between the periods are undertaken in order to identify any emerging trends.discrete choice experiments, health economics

Estimating end-use demand: A Bayesian approach

Eliminating negative end-use or appliance consumption estimates and incorporating direct metering information into the process of generating these estimates; these are two important aspects, of conditional demand analysis (CDA) that will be the focus of this raper. In both cases a Bayesian approach seems a natural way of proceeding. What needs to be investigated is whether it is also a viable and effective approach. In addition, such a framework naturally lends itself to prediction. Our application involves the estimation of electrical appliance consumptions for a sample of Australian households. This application is designed to illustrate the viability of a full Bayesian analysis of the problem

Estimating end-use demand: A Bayesian approach.

Eliminating negative end-use or appliance consumption estimates and incorporating direct metering information into the process of generating these estimates; these are two important aspects, of conditional demand analysis (CDA) that will be the focus of this raper. In both cases a Bayesian approach seems a natural way of proceeding. What needs to be investigated is whether it is also a viable and effective approach. In addition, such a framework naturally lends itself to prediction. Our application involves the estimation of electrical appliance consumptions for a sample of Australian households. This application is designed to illustrate the viability of a full Bayesian analysis of the problem.End-use demand; Direct metering; Non-negative estimates; Bayesian conditional demand analysis;

Why worry about awareness in choice problems? Econometric analysis of screening for cervical cancer

Cervical cancer is one of the most preventable and curable forms of cancer. Since 1991 there has been a concerted effort in Australia to recommend and encourage women to have Pap smears every two years. Part of the success of this National Cervical Screening Program can be gauged by exploring the determinants of screening for cervical cancer among high-risk women and by addressing the specific question of whether screening is associated with socio-economic status. Accessibility to health services remains a core goal in health policy in Australia but evidence on whether the goal is being met is limited. Using unit record data from the 1995 National Health Survey, an econometric model is developed for whether women have ever screened or not. A proportion of women in the sample contend that they have never heard of a Pap test. The analysis characterizes this group of women and accounts for their presence in our modellingScreening choice; Awareness; Censored probit; Cervical cancer

Hips and hearts: The variation in incentive effects of insurance across hospital procedures

The separate identification of effects due to incentives, selection and preference heterogeneity in insurance markets is the topic of much debate. In this paper, we investigate the presence and variation in moral hazard across health care procedures. The key motivating hypothesis is the expectation of larger causal effects in the case of more discretionary procedures. The empirical approach relies on an extremely rich and extensive dataset constructed by linking survey data to administrative data for hospital medical records. Using this approach we are able to provide credible evidence of large moral hazard effects but for elective surgeries only

Discrete Choice Experiments: A Guide to Model Specification, Estimation and Software

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 endeavour to not only provide guidance on choice modelling but to do so in a way that provides a ‘way in’ for researchers to the practicalities of data analysis. We argue that choice of modelling 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 to researchers not only within but also beyond health economics