Sentence examples for considering multiple variables from inspiring English sources

To analyze the predictors, two regression analyses were conducted considering multiple variables, such as depression, as covariates.

Because long-term results from larger studies are still lacking, at present an individualized approach for timing of total gastrectomy considering multiple variables should be discussed with each CDH1 mutation carrier.

It works on statistical machine learning principle, deriving maximum margin classification decision boundary while considering multiple variables, which in the present work has been plant specific dinucleotide density profiles variations with respect to the possible target position.

Rather than being inconsistent with the TS alone and mortality findings, these new findings enhance our understanding of elevated iron markers and morbidity and mortality and allow us to consider the more complex, but real, situation of patients by considering multiple variables together rather than independently.

Furthermore, it could consider multiple variables during the optimization process using principles of operations research and systems analysis.

Their model considers multiple variables including: climate records, population density, irrigation and industry. .

These examples indicate that host responses to climate change are multifactorial and their quantitative prediction must consider multiple variables simultaneously, including temperature, precipitation, altitude, and location.

Orthognathic surgery, which includes several procedures of varying complexity requiring high surgical skill, demands that surgeons consider multiple variables, such as the patient's overall physical condition, operating time, intraoperative hemorrhage, postoperative pain, and potential postoperative sequelae and complications.

Grey relational analysis and the desirability approach are applied to optimize the input parameters by considering multiple output variables simultaneously.

This map allows for the analysis of the network activity in a reduced system by considering only the effect of the synaptic inputs on cycle length, rather than considering multiple dynamic variables.

The consequences are estimated using dynamic loss functions developed considering multiple key state variables.