Among 1834 study participants there were multiple statistically significant relationships.
When we analyzed these variables, as well as other factors, we found multiple statistically significant relationships.
All Ferrans-Powers QLI domains, except Family, which had one statistically significant relationship, had multiple strong statistically significant relationships with cosinor and actigraphy parameters of circadian organization.
In multiple regression models, these significant relationships held, even after controlling for the duration of amenorrhea and AN.
Multiple regression analysis demonstrated significant relationships between increasing age and fewer planned contacts (Beta = −0.321, p < 0.001), whilst the relationship with diabetes duration was not significant (overall model fit R = 0.118).
We describe techniques that operate in this symbolic domain to discover rhythms, transient patterns, abnormal changes in entropy, and clinically significant relationships among multiple streams of physiological data.
The results of multiple regression analyses demonstrate statistically significant relationships between the classification of natural attenuation potential, its component properties, and baseflow river nitrate concentration.
The multiple logistic regression models showed significant relationships between falls and fall-related fractures and SRRST categories (Table 3).
The multiple logistic regression models showed significant relationships between physical performances and the demand for personal care (table 3).
After correction of p values for multiple comparisons, there were no significant relationships for the RA group between measures of disease activity and the urinary NOx, serum NOx, or urinary NOx clearance.
