A multivariate analysis involving thousands of closely sampled landmarks further identified extended and well-defined facial regions sharing similar patterns of variability, with heritability estimates reaching or exceeding 0.7, including the chin, nasal regions, nasolabial folds, upper lips and zygomatic bones.
GPS NMR thus differs markedly from previous combinations of protein NMR with multivariate analysis involving isotope-labeling and/or advanced spectral assignment protocols [5], [6].
The multivariate analysis involved all variables that were significantly related to survival on bivariate analysis at p < 0.1.
Multivariate analysis involved regression of variables that showed a univariate regression p-value of <0.1 with adjustment for potential confounders (age).
Multivariate analysis involved backward stepwise logistic regression with all variables significant at p<0.10; only variables which improved model fit at p<0.10 were included in the final model.
Step two was multivariate analysis involving running a Cox regression model for each study period by including all of its potential significant factors.
Multivariate analysis involved using binomial, multinomial and ordinal exact logistical regression models (hierarchical) and discrimination power was evaluated using area under the ROC curve.
We were able to show that MME expression is a highly significant, independent adverse prognostic factor in surgically treated lung adenocarcinoma patients in multivariate analysis involving tumor stage and MME status.
However, a multivariate analysis involving only those who developed NODAT 2 or more weeks after the OGTT revealed a similarly significant association between a normal OGTT and a reduced risk for "delayed" NODAT.
The multivariate analysis involved a stepwise multiple linear regression model, using as dependent variables those resulting from the differences in evaluation of the target organ lesions between the two measures (Difference IMT = IMT2-IMT1, Difference PWV = PWV2-PWV1, Difference PDVCORNELL = PDVCORNELL2- PDVCORNELL1, and Difference ABI = ABI2-ABI1).
