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Understanding and using time series analyses in addiction research

Beard, E; Marsden, J; Brown, J; Tombor, I; Stapleton, J; Michie, S; West, R; (2019) Understanding and using time series analyses in addiction research. Addiction , 114 (10) pp. 1866-1884. 10.1111/add.14643. Green open access

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Abstract

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 determination, 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 emphasises the importance of pre‐registering hypotheses and analysis plans before the analyses are undertaken.

Type: Article
Title: Understanding and using time series analyses in addiction research
Location: England
Open access status: An open access version is available from UCL Discovery
DOI: 10.1111/add.14643
Publisher version: https://doi.org/10.1111/add.14643
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: time series, ARIMA, ARIMAX, VAR, SVAR, VECM, addiction
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Brain Sciences > Div of Psychology and Lang Sciences > Clinical, Edu and Hlth Psychology
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health
UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Epidemiology and Health > Behavioural Science and Health
URI: https://discovery.ucl.ac.uk/id/eprint/10073471
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