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Model 1: together, without covariate adjustments; Model 2: mutually adjusted for race gender; Model 3: mutually adjusted for race, gender, age; Model 4: mutually adjusted for race, gender, age, bmi.
We ran three model specifications for all outcomes: model 1 included no adjustments; model 2 was adjusted for age (continuous), sex, socioeconomic status, and residence size; and model 3 was additionally adjusted for marital status, chronic disease, trait anxiety, as well as continuous variables for neighborhood socioeconomic disadvantage and population density.
After full adjustments (model 4) no associations remained.
Existing inequality was robust to further adjustments (Model 2) for most data.
Further adjustments (model 4) were then made for physical activity (continuous) and for intakes of meat, fish, bread, vegetables, fruit, coffee, and tea (continuous).
The multivariate analysis showed that symptoms of depression/anxiety at T1 remained significantly associated with MHS utilization at T2 after adjustments (model 1).
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This initiative would make available the primary data, all post-data collection adjustments, models including covariates used for farcasting and forecasting, and necessary documentation to the public.
In the first adjustment model, we obtained adjusted sensitivities S1 simply by averaging the four quarter sensitivities.
Associations decreased after adjusting for age, and in general, further adjustment (model 1 and model 2) provided similar estimates.
We first adjusted the PM intensity for 15 stamen microarray samples using our adjustment model, and then performed PDNN [ 15].
If an academic could tackle something like a seasonal adjustment model, that could be even more useful for making head-to-head comparisons.
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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