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The change in severity of clinical arthritis between treatments was tested by using robust linear regression from the MASS R-package.
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The regression-coefficients were then estimated using robust linear regression (Diedrichsen and Shadmehr, 2005), correcting for movement artifacts by down-weighting noisy images.
The linear approximation obtained using robust linear regression is shown as solid black lines.
CpG sites related to maternal smoking during the first trimester were identified using robust linear regression.
For CAC progression, we calculated β-coefficients (units = change in CAC volume score/year) using robust linear regression.
We use robust linear regression to measure transmissibility and source of SDRMs, which does not require dense sampling.
As the number of segments with disease was not normally distributed, the comparison between the number and subtype of coronary artery segments with atherosclerosis in the two patients groups was performed by linear regression using robust variance estimates.
Unadjusted and adjusted Prevalence Proportion Ratios (PPRs) of unwanted pregnancy and associated 95%% confidence intervals were estimated by generalized linear models with link (log) and family (Poisson) using robust variance estimator [ 43, 44].
PPRs) of unwanted pregnancy and associated 95%% confidence intervals were estimated by generalized linear models with log link function and Poisson family distribution using robust variance estimator.
The linear association between outcomes and year was estimated using robust standard errors to allow for repeated measures.
Gene significance was then estimated using a robust linear model corrected by a moderated t-test (empirical Bayes) [ 62].
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