Sample size requirements to evaluate policies in addiction research using interrupted time series analysis (ITS): Tools and guidance

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.