A matching framework to improve causal inference in interrupted time-series analysis.
1Linden Consulting Group, LLC, San Francisco, CA, USA.
Journal of Evaluation in Clinical Practice
|December 22, 2017
Summary
A new method, Interrupted Time-Series Analysis Matching (ITSAMATCH), effectively creates control groups for evaluating interventions. ITSAMATCH offers simpler interpretation and application compared to other methods like synthetic controls.
Area of Science:
- Epidemiology
- Biostatistics
- Health Services Research
Background:
- Interrupted Time-Series Analysis (ITSA) is vital for evaluating interventions on a single unit.
- Without a control group, ITSA's internal validity is often compromised.
- A comparable control group is essential for establishing counterfactuals in ITSA.
Purpose of the Study:
- Introduce ITSAMATCH, a novel framework for creating control groups in ITSA.
- Enhance the internal validity of ITSA by incorporating a matched control group.
- Compare ITSAMATCH to existing methods like synthetic controls (SYNTH) and regression adjustment.
Main Methods:
- ITSAMATCH creates control groups by matching on covariates.
- The study evaluated California's Proposition 99 using ITSAMATCH.
- Compared ITSAMATCH with SYNTH and regression adjustment based on covariate balance and treatment effects.
Main Results:
- ITSAMATCH and SYNTH achieved comparable covariate balance and treatment effect estimates.
- Regression adjustment failed to detect a treatment effect and showed inconsistent covariate adjustment.
- ITSAMATCH demonstrated effectiveness in creating a valid control group.
Conclusions:
- ITSAMATCH provides results comparable to SYNTH but is less complex and easier to interpret.
- Regression adjustment may obscure true treatment effects.
- ITSAMATCH is recommended as a primary method for evaluating interventions in multiple-group time-series analysis.
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