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In the developed model we adjust the volume of a single fish-tank to the prescribed values of stocking density, by controlling the necessary volume in each time step.
Within our model we adjust for age, education level, whether the subject has ever worked in a job known to entail exposures associated with lung cancer (i.e. List A [ 14]), and for the centre where the data was collected.
Incorporating this interval into our previously developed lung cancer risk model, we adjust for the effect of disease status-related eligibility criteria in predicting the number of lung cancer deaths in the control arm of CARET.
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In multivariable adjusted model, we adjusted for age, sex, body mass index (BMI), smoking status, race, and tumour stage.
In addition to computing an age-adjusted model, we adjusted for a priori potential confounders of the association between GDM and hypertension.
For each model, we adjusted Δ to set the Ψ V) functions of the QIF and EIF models to have the same radius of curvature at their minimum (see Methods).
In the first model, we adjusted for age only.
In multivariable model 1 (basic model) we adjusted for sex, age, center and intervention group.
We ran four models: in the first model we adjusted for the basic variables age, smoking, and study center; in the second model we adjusted for the basic variables and waist circumference; in the third model we adjusted for the basic variables and M/ I; and in the fourth model we adjusted for the basic variables, waist circumference, and M/ I.
In each model we adjusted for serum cholesterol and triglycerides, education, exercise, and smoking.
In the primary model, we adjusted the analyses for sex and year of birth.
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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