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These models used robust SEs, clustering by respondent and adjusting for time point, age, sex, race, income, and intervention arm.
Regression models used robust standard errors that account for clustering of observations within community districts (59 named areas of New York City, governed by community boards).
Analysis of accuracy and latency data was conducted using logistic and linear models, respectively; both models used robust standard errors to account for clustering by participant.
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To reassess our models we employed standard regression models using robust standard errors.
Additional models using robust regression diagnostics were created by excluding outliers with Cook's distance >1.
We re-assessed models using robust methods to account for non-independence of events within subjects.
We fitted all adjusted models using robust standard errors to take into account the clustering of siblings.
All of our models use robust standard errors which adjust for this complex sample design by clustering at the PSU level.
As the study design includes related individuals, who are not truly independent, we performed our regression models using robust standard errors to allow for any family clustering effect.
Associations between GDM and expression were then adjusted for the covariates included in our methylation models, using robust SEs in our Wald tests.
Modified Poisson regression models using robust error variance were used to estimate relative risks using the ten dichotomised outcomes and work-related injury as the main explanatory variable [ 25].
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