Health Policy and Management, School of Public Health, Yale University, USA.
Choice Modelling Centre & Institute for Transport Studies, University of Leeds, United Kingdom.
J Health Econ. 2019 May;65:93-102. doi: 10.1016/j.jhealeco.2019.03.011. Epub 2019 Apr 2.
In health, stated preference data from discrete choice experiments (DCEs) are commonly used to estimate discrete choice models that are then used for forecasting behavioral change, often with the goal of informing policy decisions. Data from DCEs are potentially subject to hypothetical bias. In turn, forecasts may be biased, yielding substandard evidence for policymakers. Bias can enter both through the elasticities as well as through the model constants. Simple correction approaches exist (using revealed preference data) but are seemingly not widely used in health economics. We use DCE data from an experiment on smokers in the US. Real-world data are used to calibrate the scale of utility (in two ways) and the alternative-specific constants (ASCs); several innovations for calibration are proposed. We find that embedding revealed preference data in the model makes a substantial difference to the forecasts; and that how models are calibrated also makes a substantial difference.
在健康领域，离散选择实验（DCE）的陈述偏好数据通常被用于估计离散选择模型，然后用于预测行为变化，其目的通常是为政策决策提供信息。DCE 数据可能存在假设偏差。反过来，预测可能存在偏差，从而为政策制定者提供低质量的证据。偏差可能同时通过弹性和模型常数进入。存在简单的校正方法（使用显示偏好数据），但在健康经济学中似乎没有得到广泛应用。我们使用了来自美国吸烟者实验的 DCE 数据。实际数据用于校准效用的标度（两种方式）和特定替代常数（ASCs）；提出了几种校准创新方法。我们发现，将显示偏好数据嵌入模型中，对预测有很大影响；而且模型的校准方式也有很大影响。
