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Multilevel Matching in Natural Experimental Studies: Application to Stepping up Counties
Niloofar Ramezani1, Alex Breno2, Jill Viglione3
1Department of Statistics, George Mason University, 4400 University Drive, Fairfax, VA 22030.
Proceedings. American Statistical Association. Annual Meeting
|April 12, 2021
Summary
This study addresses selecting control groups for natural experiments with hierarchical data. It uses advanced statistical models to identify matched counties for improving mental health services and reducing jail use.
Area of Science:
- Public Health
- Health Services Research
- Biostatistics
Background:
- Selecting appropriate control groups is challenging in natural experiments, particularly with clustered or hierarchical data.
- Lack of treatment randomization necessitates robust statistical methods to control for individual differences.
- Counties are nested within states, exhibiting clustering on health and social indicators influencing service improvement efforts.
Purpose of the Study:
- To evaluate the impact of targeted county-level efforts on mental health services and jail utilization.
- To develop and present methods for identifying matched control counties in hierarchical natural experiments.
- To account for the nested structure of state and county data in study design.
Main Methods:
- Utilizing a natural experimental study design with a hierarchical data structure.
- Employing multivariable models to adjust for observed covariates between treatment and control groups.
- Applying shrinkage-based LASSO for variable selection and logistic models for analysis.
Main Results:
- The study presents a blend of probability-based models to identify matched control counties.
- Methods are designed to account for the hierarchical structure inherent in state and county data.
- The approach facilitates the comparison of targeted interventions across different county groups.
Conclusions:
- The proposed methods enhance the rigor of natural experiments involving hierarchical data.
- Accurate matching of control counties is crucial for evaluating interventions in public health.
- This framework supports evidence-based decision-making for mental health service improvements and jail reduction strategies.
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