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The adjusted means at time 1 (immediately after intervention completion) and time 2 (3 months after intervention completion) and the mean differences within and between groups were estimated from the statistical model using estimated linear combinations.
In summary and conclusion, the method presented herein to predict creatinine clearance and urinary protein excretion based on a model using estimated urinary creatinine excretion determined by measurement of body cell mass by bioimpedance (BIA) technique has proven to be both accurate and convenient to quantify renal function in normal and diseased states.
We conducted a variance decomposition on the total variance of TEE from this model using estimated mean squares from a Type I ANOVA Table, calculated using the proc mixed procedure from SAS [ 28], and methods described in Searle et al. [ 44].
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In each individual path analysis the evolutionary model used estimated two ω ratios.
The optimal model used estimated GFR by the Chronic Kidney Disease Epidemiology Collaborative (CKD-EPI) creatinine-cystatin C equation (R 2 = 0.580).
The TACs were then randomized ×10 by injecting pseudorandom Poisson noise and refit with GDC models using estimated Poisson loss functions to recover the altered (test) parameters.
We also assessed the degree of correlation among latent factors in the model using estimates generated by AMOS.
In the fully adjusted model using estimates of exposure from 1996, the OR5ppb was 1.31 (95% CI, 1.00 1.71).
We calculated the likelihood of each respondent choosing as he/she did on each task, by applying a logit model using estimates of the respondent's utilities.
For example, in a reduced model using estimates of exposure for 2006, adjusted only for age and individual-level educational status, the OR5ppb was 1.21 (95% CI, 0.94 1.57).
The model uses estimates of pollutant exchange obtained from velocity measurements in experiments with various regular obstacle arrays.
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CEO of Professional Science Editing for Scientists @ prosciediting.com