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The independent predictors of change in leukocyte telomere length retained in the final linear regression model were: baseline telomere length (T/S), the square of baseline telomere length (T/S) 2, age, male sex, and waist-to-hip ratio.
The independent predictors of leukocyte telomere shortening retained in the final logistic regression model were: baseline telomere length (T/S) (OR per SD = 7.6; 95% CI 5.5, 10.6), age (OR per 10 year increase = 1.6; 95% CI 1.3, 2.1), male sex (OR = 2.4; 95% CI 1.3, 4.7), and waist-to-hip ratio (OR per 0.1 increase = 1.4; 95% CI 1.0, 2.0).
Therefore, the fixed effects included in the model were baseline, treatment, number of the visit, time of measurement, and the interaction between treatment and time of measurement.
At all time points the variables included in the final multivariate model were baseline values of disease duration, DAS28 score, CRP, DXR MCI, HAQ, radiographic damage and treatment group (dummy variable), together with age and gender.
Variables that were included in the multivariate model were: baseline score, diabetes, sex, age, education, congestive heart failure, previous coronary artery bypass surgery (CABG), previous percutaneous transluminal coronary angioplasty (PTCA), ventricular fibrillation, recurrent ischemia, previous angina, and hypercholesterolemia.
The variables included in the "maximum model" were baseline weight, TB history, prior exposure to ATT, extent of radiological infiltrates (unilateral vs. bilateral), presence of cavitary lesions on the chest X-ray, and semi-quantitative bacillary load determined by microscopy.
Similar(51)
Conventional water (Model 1) and CO2 (Model 2) flood models were baseline cases.
The selected time-invariant covariates included in these models were baseline age, ethnicity, pubertal Tanner stage, fat mass, and lean mass.
Put differently, exponential random graph models are baseline models of a network by assuming that all realized networks are maximally 'random' given the average values of their statistics [56].
The parameters of the model were Bicar0 (baseline), kout and A50.
We note that we only use the first person tweets for testing hypothesis and the "First person only" model is our baseline 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