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We used multivariable regression models to adjust for both clinical and sleep-related confounders.Two hundred eighteen patients were included in the analysis.
We use hierarchical multilevel models to adjust for precinct-level variability, thus directly addressing the question of geographic heterogeneity that arises in the analysis of pedestrian stops.
The authors used multivariable regression models to adjust for prespecified clinical confounders.Two hundred eighty-one patients were included in the analysis.
Government statisticians use models to adjust the raw data for seasonally recurring effects, such as extra-strong retail sales in December or slack construction in the winter.
We classified participants by nonadherence or NSAIDs and used time-varying Cox proportional hazard models to adjust for confounding.
We fit Bayesian Poisson regression models to adjust for potential risk factors, including one relatively discrete environmental exposure, and to identify areas associated with elevated risk of ALS.
In further analyses we included the matching variables as controls in the regression models to adjust for the remaining imbalance in the matched samples.
We used Cox proportional-hazards models to adjust for potential confounding variables.
It is also straightforward to incorporate covariates in the regression models to adjust for potential confounding by population stratification.
We used two multivariate models to adjust for potential confounders.
All studies used multivariate models to adjust for potential confounders.
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