ArticleAddiction (Abingdon, England)2026
Sample size requirements to evaluate policies in addiction research using interrupted time series analysis (ITS): Tools and guidance.
Article in Addiction (Abingdon, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Impact of alcohol excise taxation and structural reforms on per capita consumption in Thailand, 1995-2021: an interrupted time-series analysis.The International journal on drug policy · 2026Article
- Sample size requirements to evaluate policies in addiction research using interrupted time series analysis (ITS): Tools and guidance.Addiction (Abingdon, England) · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
Formal power calculations are rarely presented in interrupted time-series (ITS) studies due to their technical complexity, creating a significant gap in methodological rigor. This paper aimed to make power and sample size determination more accessible for researchers, particularly in the field of addiction, by providing a suite of practical and user-friendly tools. A set of resources was developed using Monte Carlo simulation to allow researchers to estimate statistical power under a wide range of ITS design parameters. The approach allows for the explicit definition of the data-generating process, including specific autocorrelation error structures (ARMA), the presence of covariates and trends and different intervention effect types (step, pulse and trend change). The study produced three key resources: (1) a flexible R code base for conducting custom power simulations, (2) an intuitive, interactive R Shiny App that enables code-free power analysis through a web interface and (3) a series of pre-calculated look-up tables for quick sample size estimation during the initial stages of study design. Illustrative examples from addiction research demonstrate the tools' application. The provided tools bridge a critical gap by simplifying the process of conducting rigorous power calculations for ITS designs. Their adoption can enhance the planning, execution and interpretation of quasi-experimental studies, helping to ensure that research is adequately powered to detect meaningful policy and intervention effects.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.