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Understanding and using time series analyses in addiction research.

Emma Beard1,2, John Marsden3, Jamie Brown1,2

  • 1Research Department of Clinical, Educational and Health Psychology, University College London, London, UK.

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Summary

This paper guides addiction researchers on using time series analyses to study trends and associations. It overviews statistical methods like ARIMA and VAR, aiding in study design and interpretation for publication.

Keywords:
ARIMAARIMAXAddictionSVARVARVECMtime series

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Area of Science:

  • Addiction research
  • Statistical modeling
  • Epidemiology

Background:

  • Time series analyses are crucial for understanding trends in repeated measurements, accounting for temporal data structures.
  • Addiction research frequently employs multiple time series and interrupted time series designs to examine variable associations and intervention impacts.

Purpose of the Study:

  • To provide addiction researchers with a comprehensive overview of available time series analysis methods.
  • To offer guidance on the appropriate application, sample size determination, reporting, and interpretation of these methods.
  • To enhance clarity for researchers regarding publication standards in journals like Addiction.

Main Methods:

  • Overview of statistical methods including Generalized Linear Models (GLM), Generalized Linear Mixed Models (GLMM), Generalized Least Squares (GLS), Generalized Additive Mixed Models (GAMM), Autoregressive Integrated Moving Average (ARIMA), ARIMA with eXogenous variables (ARIMAX), Vector Autoregression (VAR), Structural Vector Autoregression (SVAR), and Vector Error Correction Models (VECM).

Main Results:

  • The paper details various analytical tools, highlighting their individual strengths and limitations.
  • Guidance is provided on selecting appropriate methods based on research questions and data characteristics.
  • Emphasis is placed on the importance of pre-registering hypotheses and analysis plans to ensure methodological rigor.

Conclusions:

  • This overview aims to demystify complex time series analyses for addiction researchers.
  • Adherence to methodological guidance and pre-registration can improve the quality and reproducibility of addiction research.
  • The paper serves as a valuable resource for researchers aiming for publication in leading addiction journals.