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Correlations obtained through multivariable regression analysis were tested on randomly generated different fracture network types and natural fracture patterns.
With their database of almost 600,000 foul calls, Mr. Wolfers and Mr. Price used a common statistical technique called multivariable regression analysis, which can identify correlations between different variables.
The significance of each factor and the two-way interactions between them was studied by multivariable regression analysis.
Multivariable regression analysis demonstrated a 1.89 increased relative risk of relapse for individuals given IVIg (p = 0.021).
Hazard ratios with 95% confidence intervals to estimate the risk of death were calculated using Cox multivariable regression analysis.
We will use multivariable regression analysis to evaluate the impact of the intervention on primary and secondary outcomes.
Multivariable regression analysis confirmed ICU admission undetectable VD concentration (p = 0.01) as independent predictor of sepsis-related mortality.
In addition, the demographic and preoperative characteristics that served as significant predictors of sepsis were identified using multivariable regression analysis.
If two variables were determined to be collinear [21], only one was included in the multivariable regression analysis.
Multivariable regression analysis associated heart failure, hypertension, renal insufficiency, and type of procedure as risk factors for the development of leak (p < 0.05).
Multivariable regression analysis after adjusting for patient characteristics was performed and generalized estimating equations were used to account for within-hospital clustering.
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