Multivariate analyses have consistently identified a number of patient, tumor, and treatment variables that are significant prognostic factors for overall survival in patients with high-grade glioma.
Multivariate analyses have shown a significant increase in rate of polyethylene wear in patients with excellent Harris hip score and younger age [27], and increased rate of wear has been likely related to level of activity in younger patients.
Multivariate analyses have been shown to be appropriate and helpful tools for grouping forest stands by fertility classes (i.e. soil management zones using the terminology of precision agriculture).
In multivariate analyses having missed zidovudine doses was positively associated with prenatal illicit drug use (odds ratio, 3.49; 95% confidence interval, 1.30-9.42; P <.05) and missing prenatal vitamins (odds ratio, 2.71; 95% confidence interval, 1.30-5.67; P <.01).
Multivariate analyses have been widely adopted in linkage studies [59], [60], [61], [62], [63], and it has been generally accepted that multivariate analyses outperform univariate analyses in terms of increasing statistical power and precision of parameter estimation [64], [65].
Of the variables chosen for the univariate analyses, four were significant and entered into the multivariate analyses: having two protocols (p = 0.001); an updated review (p<0.0001), number of authors (p = 0.008); and number of primary outcomes (p = 0.002).
The three parameters identified here using both single parameter and multivariate analyses have been combined into a single predictive equation of the effect of GAGs on the kinetics of amyloid formation.
Both bivariate and multivariate analyses have been performed to describe the fertility differentials.
However, observational studies using multivariate analyses have identified factors predictive of PSA recurrence and disease outcome.
