Data for: Comparative evaluation of interrupted time series analytical methods for healthcare quality improvement research: a Monte Carlo simulation study

This dataset supports a Monte Carlo simulation study comparing four interrupted time series (ITS) analytical methods—ARIMAX, GAM, Prophet, and LSTM—for healthcare quality improvement research. The simulation uses a full 2⁹ factorial design with 512 scenarios and 500 replicates per scenario (256,000 simulated time series in total). The nine factors are time series length, AR(1) coefficient, level- and slope-change intervention effects, trend type, seasonality, noise standard deviation, external shock, and number of covariates. Each replicate is stored as a CSV file under `scenario_<id>/rep_<j>.csv`; `scenarios_key.csv` maps each scenario ID to its factor levels. The complete analysis code, environment specifications, and step-by-step reproducibility guide are available at https://github.com/SeungmanKim/ITS. Full methodological details are described in the accompanying manuscript, currently under peer review at BMC Medical Research Methodology. License: Creative Commons Attribution 4.0 International (CC BY 4.0).