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A Comprehensive Simulation Study to Evaluate the Effect Size and Study Length Relationship in Single-Group

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Determining sample size for interrupted time-series analysis (ITSA) is crucial. This study provides guidance on the number of time periods needed for sufficient statistical power in healthcare research, considering autocorrelation and intervention timing.

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Area of Science:

  • Biostatistics
  • Health Services Research
  • Epidemiology

Background:

  • Single-group interrupted time-series analysis (ITSA) is widely used in healthcare research.
  • Limited guidance exists for determining adequate sample size (number of time periods) for ITSA studies.
  • Power requirements are critical for detecting statistically significant intervention effects.

Purpose of the Study:

  • To estimate the number of time periods required for ITSA studies to achieve >80% and >90% statistical power.
  • To assess the impact of autocorrelation, intervention timing, and effect size on power requirements.
  • To provide practical tools for researchers to determine sample size for ITSA.

Main Methods:

  • Simulations were conducted using varying numbers of time periods (10-100).
  • Autocorrelation, intervention timing (33%, 50%, 67%), and effect sizes (level and trend changes) were manipulated.
  • Statistical significance (p < 0.05, p < 0.01) and power levels were evaluated.

Main Results:

  • Shorter studies and higher autocorrelation necessitate larger effect sizes for significance.
  • Earlier or later intervention introduction (than 50%) requires larger effect sizes.
  • Estimating level changes generally requires smaller effect sizes than trend changes, except with many time periods.
  • Studies with only 10 time periods yielded unreliable estimates.

Conclusions:

  • Healthcare researchers can use simulation-derived tables and the POWER_ITSA Stata package to plan ITSA studies.
  • Adequate power depends on study length, autocorrelation, intervention timing, and effect size.
  • Avoid studies with fewer than 10 time periods due to unreliable results.