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Constructing inverse probability weights for continuous exposures: a comparison of methods.
Ashley I Naimi1, Erica E M Moodie, Nathalie Auger
1From the aDepartment of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, QC, Canada; and bInstitut national de santé publique du Québec, and Research Centre of the University of Montreal Hospital Centre, Montreal, QC, Canada.
Constructing inverse probability weights for continuous exposures is challenging. Quantile binning, gamma, and heteroscedastic normal distributions performed best for modeling continuous exposures in epidemiological studies.
Area of Science:
- Epidemiology
- Biostatistics
- Statistical Modeling
Background:
- Inverse probability-weighted marginal structural models are common for binary exposures in epidemiology.
- Constructing weights for continuous exposures is complex due to outliers and nonconstant variance.
Purpose of the Study:
- To evaluate different methods for constructing inverse probability weights for continuous exposures.
- To compare the performance of various weighting approaches using Monte Carlo simulations.
Main Methods:
- Simulated continuous exposures and binary outcomes from a large empirical cohort.
- Assessed six methods: normal, heteroscedastic normal, truncated normal, gamma, t-distribution, and quantile binning.
- Estimated marginal odds ratios and computed bias and mean squared error.
Main Results:
- For homoscedastic exposures, standard normal, gamma, and quantile binning performed best.
- For heteroscedastic exposures, quantile binning, gamma, and heteroscedastic normal approaches were superior.
- Quantile binning demonstrated simplicity and versatility.
Conclusions:
- Quantile binning is a robust and adaptable method for creating inverse probability weights with continuous exposures.
- The choice of method depends on the exposure's distributional characteristics (homoscedastic vs. heteroscedastic).
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