F Reed Johnson, Wiktor Adamowicz, Catharina Groothuis-Oudshoorn
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引用次数: 0
摘要
本文利用一个简单离散选择实验（DCE）的示例数据，介绍了选择数据的统计分析。它介绍了分析数据集的布局、数据集中包含的变量类型，以及如何识别数据中表明数据质量的响应模式。模型规范选项包括具有连续属性水平的线性模型以及非线性连续和分类属性水平模型。讨论了条件 logit、混合 logit 和潜类分析的优缺点，并使用 DCE 数据示例进行了说明。为读者提供了用于分析选择数据的各种软件程序的链接。对于目前共识有限的主题和更先进的技术，我们还提供了参考文献，以指导有兴趣深入探讨选择建模难题的读者。补充材料包括用于说明建模方法的模拟示例数据，以及重现所示估计值的 R 和 Matlab 代码。
What Can Discrete-Choice Experiments Tell Us about Patient Preferences? An Introduction to Quantitative Analysis of Choice Data.
This paper provides an introduction to statistical analysis of choice data using example data from a simple discrete-choice experiment (DCE). It describes the layout of the analysis dataset, types of variables contained in the dataset, and how to identify response patterns in the data indicating data quality. Model-specification options include linear models with continuous attribute levels and non-linear continuous and categorical attribute levels. Advantages and disadvantages of conditional logit, mixed logit, and latent-class analysis are discussed and illustrated using the example DCE data. Readers are provided with links to various software programs for analyzing choice data. References are provided on topics for which there currently is limited consensus and on more advanced techniques to guide readers interested in exploring choice-modeling challenges in greater depth. Supplementary materials include the simulated example data used to illustrate modeling approaches, together with R and Matlab code to reproduce the estimates shown.
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The Patient provides a venue for scientifically rigorous, timely, and relevant research to promote the development, evaluation and implementation of therapies, technologies, and innovations that will enhance the patient experience. It is an international forum for research that advances and/or applies qualitative or quantitative methods to promote the generation, synthesis, or interpretation of evidence.
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