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Four conditions "related", "unrelated", "perceptual", and "baseline" were modeled using a canonical HRF (hemodynamic response function).
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The change in disability count outcome between follow-up and baseline was modeled using linear regression.
The time course of CYP3A4 amount relative to a nominal baseline was modelled using a turnover model (Equation 8), assuming an inducible zero-order production rate (Rin) and a first order turnover rate (kout × CYP3A4) 37. The turnover rate constant kout was fixed using a turnover half-life of CYP3A4 obtained from the literature (70 h) 38.
Observed triglycerides at baseline and during treatment were modeled using two approaches: a triglycerides LDR analysis (modeling the change from baseline in triglycerides as a continuous variable) and a time to event approach (modeling the time to reach triglycerides >200 mg/dL).
Experienced utilities at baseline, week 13, and week 26 were modeled using a random-effects linear model (9, 10).
The log cumulative baseline excess mortality functions for both outcomes were modeled using restricted cubic splines with 5 df.
A determination of whether the CMS and SF-36 absolute and percent changes from baseline can reasonably be modeled using Gaussian theory will be performed by examining normal probability plots.
Since there are no specific utilities estimates from Argentina for diabetes and its complications, baseline utility was modelled using the mean EQ-5D values reported by the 2003 England Health Survey for people with diabetes and without major chronic complications, stratified by age group [31].
The baseline hazard was modelled using 5 df.
The baseline hazard was modelled using 5 df for the spline variables.
The baseline cumulative risk is modelled using a restricted cubic spline with 5 knots at 5%, 25 %, 50 %, 755 %and95%5 % quantiles of the failure time distribution.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com