Power Considerations for Multiple-Group (Controlled) Interrupted Time Series Analysis: A Comprehensive Simulation
1Department of Medicine, Division of Clinical Informatics & Digital Transformation (DoC-IT), University of California, San Francisco, CA, USA.
Evaluation & the Health Professions
|March 17, 2026
Summary
This study provides guidance on power for multiple-group (controlled) interrupted time series (MG-ITSA) designs. Key factors influencing power include study length, control units, effect size, and autocorrelation.
Area of Science:
- Health research methodology
- Statistical power analysis
Background:
- Limited guidance exists for power considerations in multiple-group (controlled) interrupted time series (MG-ITSA) designs.
- MG-ITSA designs are crucial for evaluating interventions in public health and clinical research.
Purpose of the Study:
- To estimate statistical power for MG-ITSA designs based on various factors.
- To provide recommendations for optimizing power in MG-ITSA studies.
Main Methods:
- Simulations were used to estimate power.
- Factors examined included number of time periods, control units, treatment introduction timing, and autocorrelation.
- Measures of effect included difference-in-differences (DID) in level and trend.
Main Results:
- Higher power was associated with longer studies, more control units, larger effect sizes, and lower autocorrelation.
- DID in level required fewer time periods than DID in trend for desired power.
- Treatment introduction at the midpoint generally yielded higher power for DID in trend.
Conclusions:
- Researchers can increase power by increasing control units, time periods, and effect size, or by using DID in level.
- Autocorrelation must be accounted for in time series regression models.
- Consideration of these factors is essential for efficient MG-ITSA study design in health research.
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