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Using group-based trajectory modelling to enhance causal inference in interrupted time series analysis.

Ariel Linden1

  • 1Linden Consulting Group, LLC, San Francisco, CA, USA.

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Group-based trajectory modelling (GBTM) enhances interrupted time series analysis (ITSA) for improved causal inference. This method identified comparable control groups, showing California

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Area of Science:

  • Epidemiology
  • Biostatistics
  • Public Health

Background:

  • Interrupted time series analysis (ITSA) is a method for causal inference.
  • Enhancements to ITSA are needed to improve the accuracy of causal inference.
  • Group-based trajectory modelling (GBTM) is proposed as a complementary method to ITSA.

Purpose of the Study:

  • To introduce GBTM as a complement to ITSA for enhanced causal inference.
  • To utilize GBTM for comparing outcomes across trajectory groups.
  • To use GBTM to identify non-treated control units for ITSA.

Main Methods:

  • The study applied GBTM to analyze the impact of California's Proposition 99 on cigarette sales.
  • A stand-alone GBTM identified distinct cigarette sales trajectory groups from 1970-2000.
  • A second approach used baseline GBTM (1970-1988) to identify control states for ITSA.

Main Results:

  • The stand-alone GBTM identified three trajectory groups: low, medium, and high decreasing cigarette sales.
  • California and 26 other states were in the low-decreasing trajectory group.
  • Using baseline data, California and 19 non-treated states formed the low group, with California showing a significant post-intervention decrease in sales (P < 0.01).

Conclusions:

  • GBTM enhances ITSA by providing group-level outcome trajectory context.
  • GBTM aids in identifying suitable non-treated units for control in ITSA.
  • The study demonstrates GBTM's utility in causal inference for public health interventions.