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Stacking Weights and Model Space Selection in Frequentist Model Averaging for Benchmark Dose Estimation
https://orcid.org/0000-0001-7114-0615
ABSTRACTIn dose‐response modeling, several models can often yield satisfactory fits to the observed data. The current practice in risk assessment is to use model averaging, which is a way to combine multiple models in a weighted average. A key parameter in risk assessment is the benchmark dose, the dose resulting in a predefined abnormal change in response. Current practice when applying frequentist model averaging is to use weights based on the Akaike Information Criterion (AIC). This paper introduces stacking weights as an alternative for dose‐response modeling and generalizes a Diversity Index from dichotomous to continuous responses for model space selection. Three simulation studies were conducted to evaluate the new methods. They showed that, in three realistic scenarios, recommended strategies generally performed well, with stacking weights outperforming AIC weights in several cases. Strategies involving model selection were less effective. However, in a challenging scenario, none of the methods performed well. Due to the promising results of stacking weights, they have been added to the R package “bmd.”
Stacking Weights and Model Space Selection in Frequentist Model Averaging for Benchmark Dose Estimation
https://orcid.org/0000-0001-7114-0615
ABSTRACTIn dose‐response modeling, several models can often yield satisfactory fits to the observed data. The current practice in risk assessment is to use model averaging, which is a way to combine multiple models in a weighted average. A key parameter in risk assessment is the benchmark dose, the dose resulting in a predefined abnormal change in response. Current practice when applying frequentist model averaging is to use weights based on the Akaike Information Criterion (AIC). This paper introduces stacking weights as an alternative for dose‐response modeling and generalizes a Diversity Index from dichotomous to continuous responses for model space selection. Three simulation studies were conducted to evaluate the new methods. They showed that, in three realistic scenarios, recommended strategies generally performed well, with stacking weights outperforming AIC weights in several cases. Strategies involving model selection were less effective. However, in a challenging scenario, none of the methods performed well. Due to the promising results of stacking weights, they have been added to the R package “bmd.”
Stacking Weights and Model Space Selection in Frequentist Model Averaging for Benchmark Dose Estimation
https://orcid.org/0000-0001-7114-0615
ABSTRACTIn dose‐response modeling, several models can often yield satisfactory fits to the observed data. The current practice in risk assessment is to use model averaging, which is a way to combine multiple models in a weighted average. A key parameter in risk assessment is the benchmark dose, the dose resulting in a predefined abnormal change in response. Current practice when applying frequentist model averaging is to use weights based on the Akaike Information Criterion (AIC). This paper introduces stacking weights as an alternative for dose‐response modeling and generalizes a Diversity Index from dichotomous to continuous responses for model space selection. Three simulation studies were conducted to evaluate the new methods. They showed that, in three realistic scenarios, recommended strategies generally performed well, with stacking weights outperforming AIC weights in several cases. Strategies involving model selection were less effective. However, in a challenging scenario, none of the methods performed well. Due to the promising results of stacking weights, they have been added to the R package “bmd.”
Stacking Weights and Model Space Selection in Frequentist Model Averaging for Benchmark Dose Estimation
Environmetrics
Baalkilde, Jens Riis (author) / Hansen, Niels Richard (author) / Jensen, Signe Marie (author)
Environmetrics ; 36
2025-03-01
Article (Journal)
Electronic Resource
English
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