Two-way fixed effects (TWFE) models are increasingly applied in air pollution epidemiology. However, it remains unclear how sensitive TWFE models are to different modeling choices and how to transparently select a final model from a wide range of options. This study aims to systematically assess the sensitivity of TWFE models to different fixed effects (FEs) and propose a comprehensive decision-making framework. As an illustrative case study, we investigated associations between weekly ZIP Code Tabulation Area (ZCTA)-level mean PM2.5 concentrations and respiratory hospitalizations in California, 2011-2019. We proposed 52 TWFE models with all plausible spatiotemporal FE combinations and demonstrated a three-stage decision-making framework for final model selection based on permutation tests, equivalence testing, and model complexity. Substantial variations in estimates were observed among models with different combinations of FEs and their interactions. The final model estimated that a 1 μg/m3 increase in weekly mean PM2.5 concentration was associated with a 0.06% increase in weekly respiratory hospitalizations (95% CI, 0.03%, 0.09%; clustered SE by ZCTA). This study highlighted that the choice of FEs in TWFE models can have meaningful influences in the inference of interest. Our proposed decision-making offers a practical guide for future air pollution epidemiological studies using TWFE models.
Ma et al. (Wed,) studied this question.