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Using machine learning to identify structural breaks in single-group interrupted time series designs.

Ariel Linden1,2, Paul R Yarnold3

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|April 20, 2016
PubMed
Summary

Structural breaks detected before an intervention challenge the validity of single-group interrupted time series analysis (ITSA). Routine sensitivity analysis using methods like optimal discriminant analysis (ODA) is recommended for robust evaluation.

Keywords:
causal inferencedata mininginterrupted time series analysismachine learningmaximum-accuracy modeloptimal discriminant analysisquasi-experimentalstructural breaks

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health Policy Evaluation

Background:

  • Single-group interrupted time series analysis (ITSA) is widely used to evaluate interventions.
  • The internal validity of ITSA relies on the assumption that observed changes are solely due to the intervention.
  • Pre-existing trends or structural breaks before an intervention can confound ITSA results.

Purpose of the Study:

  • To introduce optimal discriminant analysis (ODA) as a machine-learning method for detecting pre-intervention structural breaks.
  • To assess the impact of pre-intervention structural breaks on the validity of ITSA.
  • To evaluate California's Proposition 99 using ITSA and identify potential confounding factors.

Main Methods:

  • Employed a single-group interrupted time series analysis (ITSA) design.
  • Utilized optimal discriminant analysis (ODA), a machine-learning algorithm, to identify structural breaks in time series data.
  • Applied the methodology to data from California's Proposition 99, which aimed to reduce smoking rates.

Main Results:

  • Optimal discriminant analysis (ODA) identified significant structural breaks in the time series prior to the 1989 implementation of Proposition 99.
  • Perfect structural breaks were detected in 1983 and 1985, predating the intervention.
  • These findings raise concerns about the validity of treatment effect estimations derived from single-group ITSA for this intervention.

Conclusions:

  • Pre-intervention structural breaks can significantly undermine the internal validity of single-group ITSA.
  • Sensitivity analyses for structural breaks should be a standard component of ITSA research.
  • Integrating machine-learning techniques like ODA can enhance the rigor of time series evaluation.