Sentence examples for more traditional regression from inspiring English sources

We used the case-crossover design, as well as more traditional regression methods, to evaluate the impact of influenza and respiratory syncytial virus (RSV) on meningococcal disease risk in Central Ontario, Canada.

To compare this result with a more traditional regression statistical analysis, we also performed a multivariate regression correlation analysis between the receptor occupancies (ROs) of each drug dose combination and their respective WM outcomes.

Unlike more traditional regression models, CART models are able to identify interactions between ecological factors that best split geographical areas into homogenous subgroups based on their relative incidence rates.

Although more traditional regression analyses are useful for delineating causal relationships among variables, SEM enables more efficient characterization of complex, real-world processes than can be achieved using correlation-based analyses [ 38].

This could be used in conjunction with more traditional regression models to avoid missing potential confounders, as suggested in the follow-up options provided in Figures 1 and 4. The NHANES study design results in variables that are never measured together in an individual (e.g., some urinary chemicals).

While there could be concern that the results of our model are specific to the subjects in our study and not generalizable to the population, we specifically chose penalization methods, instead of more traditional regression methodology, with leave-one-out cross-validation, since it is a good way to fit a model that generalizes well to the population rather than to a particular set of patients.

Little is known, however, about the sample size that is needed to generate a prediction model with a modern modelling technique that outperforms more traditional, regression-based modelling techniques in medical data.

The log-binomial regression method provides relative risk estimates as a measure of association using binary outcome data which are easier to interpret than odds ratios obtained from the more traditional logistic regression model.

Different analysis methods are available when analyzing data with multiple potential predictors, these include tree analysis, neural networks, and the more traditional approach of regression modeling.

Hence, if one wants to explain an underlying process or mathematical framework that produces the relationships between the dependent and independent variables, it would be better to use a more traditional statistical model like regression analysis.

The WGCNA* mortality groups with low (5.4%), moderate (24%) and high (67%) mortality rates differed by 35% from a mortality classification developed by a more traditional step-wise Cox regression approach.