Understanding and using time series analyses in addiction research
Understanding and using time series analyses in addiction research
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DOI：
10.1111/add.14643
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发表时间：
2019-07-09
期刊：
影响因子：
6
通讯作者：
West, Robert
中科院分区：
文献类型：
--
作者：
Beard, Emma;Marsden, John;West, Robert
时间序列分析是一种统计方法，用于评估定期重复测量的趋势及其与其他趋势或事件的关联，同时考虑到此类数据的时间结构。成瘾研究通常涉及评估目标变量（例如人口吸烟率）和预测变量（例如香烟的平均价格）之间的关联，称为多时间序列设计，或干预或事件（例如引入室内禁烟令），称为中断时间序列设计。有许多可用的分析工具，每一种都有自己的优势和局限性。本文为成瘾研究人员提供了许多可用方法（GLM，GLMM，GLS，GAMM，ARIMA，ARIMAX，VAR，SVAR，VECM）的概述，并指导何时以及如何使用它们，样本量确定，报告和解释。其目的是为研究人员提供更清晰的建议，以进行这些分析，了解什么可能是可以接受的出版在期刊上，如成瘾。鉴于在建立时间序列模型时需要作出大量选择，该指南强调在进行分析之前预先登记假设和分析计划的重要性。
Time series analyses are statistical methods used to assess trends in repeated measurements taken at regular intervals and their associations with other trends or events, taking account of the temporal structure of such data. Addiction research often involves assessing associations between trends in target variables (e.g. population cigarette smoking prevalence) and predictor variables (e.g. average price of a cigarette), known as a multiple time series design, or interventions or events (e.g. introduction of an indoor smoking ban), known as an interrupted time series design. There are many analytical tools available, each with its own strengths and limitations. This paper provides addiction researchers with an overview of many of the methods available (GLM, GLMM, GLS, GAMM, ARIMA, ARIMAX, VAR, SVAR, VECM) and guidance on when and how they should be used, sample size det ermination, reporting and interpretation. The aim is to provide increased clarity for researchers proposing to undertake these analyses concerning what is likely to be acceptable for publication in journals such as Addiction. Given the large number of choices that need to be made when setting up time series models, the guidance emphasizes the importance of pre-registering hypotheses and analysis plans before the analyses are undertaken.