论文标题
酒精和药物使用数据频率的多退变延续率模型
Many-levelled continuation ratio models for frequency of alcohol and drug use data
论文作者
论文摘要
对酒精和吸毒的研究通常对人们在调查日期之前28天（例如28天）使用感兴趣的物质的天数感兴趣。尽管计数模型通常用于此目的，但由于响应变量在上面有限，因此它们并不严格适合此类数据。此外，如果某些人的物质使用行为的特征是各种每周使用模式，则较长时期使用的物质使用天数的摘要可以表现出多种模式。这些物质使用天数数据的这些特征不容易与常规参数模型家族一起拟合。我们提出了一个用于物质使用日期数据的延续比率序数模型。每个可能的值都分配了自己的类别。这允许恢复预测的顺序响应所隐含的确切数字分布。我们使用调查数据报告日期在28天的间隔内使用调查数据报告日期证明了所提出的模型。我们显示，与二项式，栏 - 阴性二项式和β二项式模型相比，持续比模型可以更好地捕获饮酒日数据集中的复杂性。
Studies of alcohol and drug use are often interested in the number of days that people use the substance of interest over an interval, such as 28 days before a survey date. Although count models are often used for this purpose, they are not strictly appropriate for this type of data because the response variable is bounded above. Furthermore, if some peoples' substance use behaviors are characterized by various weekly patterns of use, summaries of substance days-of-use used over longer periods can exhibit multiple modes. These characteristics of substance days-of-use data are not easily fitted with conventional parametric model families. We propose a continuation ratio ordinal model for substance days-of-use data. Instead of grouping the set of possible response values into a small set of ordinal categories, each possible value is assigned its own category. This allows the exact numeric distribution implied by the predicted ordinal response to be recovered. We demonstrate the proposed model using survey data reporting days of alcohol use over 28-day intervals. We show the continuation ratio model is better able to capture the complexity in the drinking days dataset compared to binomial, hurdle-negative binomial and beta-binomial models.
