Statistical methods for evaluating the correlation between timeline follow-back data and daily process data with applications to research on alcohol and marijuana use
Statistical methods for evaluating the correlation between timeline follow-back data and daily process data with applications to research on alcohol and marijuana use
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DOI：
10.1016/j.addbeh.2018.12.024
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发表时间：
2019-07-01
影响因子：
4.4
通讯作者：
Buu, Anne
中科院分区：
文献类型：
--
作者：
Liu, Wanjun;Li, Runze;Buu, Anne
背景资料：回顾性时间轴跟踪（TLFB）数据和前瞻性的日常过程数据已被频繁地收集在成瘾研究中，以表征行为模式。虽然以前的有效性研究已经证明了这两种类型的数据之间的高度相关性，在这些研究中采用的传统方法是基于总结措施，可能会失去关键信息和皮尔逊相关系数，具有不良的属性。本研究提出的功能一致性相关系数，以解决这些问题。方法：我们使用真实的数据收集的随机实验，以证明所提出的方法的应用，并比较其分析结果与传统的方法。我们还进行了模拟研究的基础上的真实的数据，以评估与传统的方法。结果：结果的真实的数据的例子表明，这两种类型的数据之间的相关性不同的物质（酒精与大麻）和评估计划（每日与每周）。此外，传统方法估计的相关性往往高于所提出的方法估计的相关性。仿真结果进一步表明，与传统方法相关联的高估的幅度是最大的，当真实的相关性是medium.Conclusions：研究结果的真实的数据的例子意味着，每天的评估是特别有益的特征更多变的行为，如酒精的使用，而每周的评估可能是足够的低变化的事件，如大麻的使用。该方法是一种较好的评价TLFB数据有效性的方法。
Background: Retrospective timeline follow-back (TLFB) data and prospective daily process data have been frequently collected in addiction research to characterize behavioral patterns. Although previous validity studies have demonstrated high correlations between these two types of data, the conventional method adopted in those studies was based on summary measures that may lose critical information and the Pearson's correlation coefficient that has an undesirable property. This study proposes the functional concordance correlation coefficient to address these issues.Methods: We use real data collected from a randomized experiment to demonstrate the applications of the proposed method and compare its analytical results with those of the conventional method. We also conduct a simulation study based on the real data to evaluate the level of overestimation associated with the conventional method.Results: The results of the real data example indicate that the correlation between these two types of data varies across substances (alcohol vs. marijuana) and assessment schedules (daily vs. weekly). Additionally, the correlations estimated by the conventional method tend to be higher than those estimated by the proposed method. The simulation results further show that the magnitude of overestimation associated with the conventional method is greatest when the true correlation is medium.Conclusions: The findings of the real data example imply that daily assessments are particularly beneficial for characterizing more variable behaviors like alcohol use, whereas weekly assessments may be sufficient for low variation events such as marijuana use. The proposed method is a better approach for evaluating the validity of TLFB data.