In alcoholism research, several complementary outcomes are of interest:abstinence from drinking during a specific time frame, and, when the individual is drinking, frequency of drinking (the proportion of days on which drinking occurs) and intensity of drinking (the average number of drinks per drinking day). The outcomes are often measured repeatedly over time on the same subject and, although they are closely related, they are rarely modelled together. We propose a joint model that allows us to fit these longitudinal outcomes simultaneously, using correlated random effects to model the association between the outcomes and between repeated measurements on the same subject. The model has three parts: a logistic part for sustained abstinence over the period of interest, a truncated binomial part for frequency of drinking and a log-normal model for drinking intensity when drinking occurs. Because of the computational impracticality of fitting models with many random effects by using standard frequentist approaches, we use a Bayesian approach to fit the joint model. We also conduct a simulation study to investigate the gains in parameter estimate bias and mean-squared error associated with joint versus separate modelling. We illustrate the approach on data from an alcoholism clinical trial.

中文翻译：

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在酒精中毒试验中禁欲，饮酒频率和强度的纵向数据的贝叶斯联合建模。

在酗酒研究中，有几个互补的结果令人感兴趣：在特定时间范围内戒酒，个人饮酒时，饮酒频率（发生饮酒的天数）和饮酒强度（平均饮酒次数）。每天喝酒）。通常会随着时间的推移反复评估同一主题的结果，尽管它们密切相关，但很少一起建模。我们提出了一个联合模型，该模型允许我们使用相关的随机效应同时拟合这些纵向结果，以对结果之间以及同一对象的重复测量之间的关联进行建模。该模型包括三个部分：在感兴趣期间持续禁欲的后勤部分，饮酒频率时，截短的二项式部分和饮酒强度的对数正态模型。由于使用标准的频度高的方法拟合具有许多随机效应的拟合模型在计算上不切实际，因此我们使用贝叶斯方法拟合联合模型。我们还进行了仿真研究，以研究与联合建模和单独建模相关的参数估计偏差和均方误差的增益。我们举例说明了酒精中毒临床试验中的数据方法。我们还进行了仿真研究，以研究与联合建模和单独建模相关的参数估计偏差和均方误差的增益。我们举例说明了酒精中毒临床试验中的数据方法。我们还进行了仿真研究，以研究与联合建模和单独建模相关的参数估计偏差和均方误差的增益。我们举例说明了酒精中毒临床试验中的数据方法。