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Inclusion of terms for total employment duration (lifetime employment as of study age) and the square of that term, produced a better fitting model with breast cancer risk declining with total employment (χ2 2df) =5.84, p=0.05).
It should be noted that using log transformed power models to "accommodate leveling-off at highest exposure levels" might be useful for getting a better fitting model with smaller p-values, but there is no plausible biological reason why the dose-response would level off if DE were a real causative agent or why HRs would remain below 1.0 below 1000 REC.
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In addition, Akaike's Information Criterion (AIC) can be used when comparing competing models, with smaller values indicating the better fitting model (43).
Model performance was assessed using Akaike's information criterion (AIC), with lower values indicating a better fitting model.
The Akaike Information Criterion (AIC) was used for model comparisons; smaller values indicate better fitting model.
It was demonstrated that the normal inverse Gaussian distribution is the better fitting model, out of those analysed, and usually better than the more commonly used K-distribution, with the exception of over sea ice.
A lower DIC indicates a better fitting model.
Through use of the hypertabastic survival model, a parametric model we attain a better fitting model.
We thus concluded that the ACE AxE het model was a better fitting model.
Lower BIC and AIC values would indicate a better fitting model.
For the AIC and BIC, smaller values indicate a better fitting model.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com