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Multilevel Conditional Autoregressive models for longitudinal and spatially referenced epidemiological data
D Djeudeu1, S Moebus2, K Ickstadt1
1Faculty of Statistics, TU Dortmund, 44221 Dortmund, Germany.
Spatial and Spatio-Temporal Epidemiology
|June 12, 2022
Summary
New multilevel models (MLM tCARs) improve spatial analysis in longitudinal epidemiological studies. These models better capture time-varying spatial effects and outperform traditional growth models, revealing a negative association between greenness and depression.
Area of Science:
- Epidemiology
- Spatial Statistics
- Longitudinal Data Analysis
Background:
- Multilevel Conditional Autoregressive (CAR) models are crucial for analyzing spatial effects in nested epidemiological data.
- Existing models often struggle with time-varying spatial structures in longitudinal studies.
Purpose of the Study:
- To develop and evaluate novel multilevel models with time-varying CAR structures (MLM tCARs) for longitudinal epidemiological data.
- To compare the performance of MLM tCARs against classical multilevel growth models.
- To provide a decision tree for analyzing spatially nested data, including cross-sectional applications (MLM CARs).
Main Methods:
- Development of Multilevel Models with time-varying CAR structures (MLM tCARs).
- Simulation studies comparing MLM tCARs with classical multilevel growth models.
- Application of MLM CARs and MLM tCARs to the Heinz Nixdorf Recall Study data.
Main Results:
- MLM tCARs demonstrated superior performance in retrieving true regression coefficients and model fit compared to classical models.
- Simulation studies confirmed the utility of MLM CARs for cross-sectional data.
- Analysis of the Heinz Nixdorf Recall Study revealed a significant negative association between greenness and depression.
Conclusions:
- MLM tCARs offer an advanced approach for analyzing longitudinal epidemiological data with complex spatial dependencies.
- The developed models and decision tree provide valuable tools for researchers studying environmental exposures and health outcomes.
- A negative association between environmental greenness and depression was identified in the study population.
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