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The linear associations between mandible shape and diet principal components were tested by multivariate regression (where each data point is a species average), using a phylogenetic generalized linear model (PGLS - [ 40, 88]).
Univariate or unadjusted regression analysis was followed by multivariate regression where patient characteristics found to be significantly associated with the total number of transitions, at the 0.05 level of significance in the unadjusted analysis were included in the initial model.
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We also evaluated the effect of the major arsenic metabolizing gene AS3MT on the association between arsenic and telomere length (first-degree relatives were excluded, total N = 166 with telomere length data) by multivariate regression analysis where we included haplotype (as categorical variable), urinary arsenic, and an interaction term between haplotype and urinary arsenic.
Associations were estimated by multivariate regression analyses.
Associations were assessed by multivariate regression analyses.
Adjustment for potential confounders was made by multivariate regression analysis.
These influences were reduced by multivariate regression analysis (Table 4).
This was followed by a multivariate regression analysis where independent variables that showed a significant effect during univariate analysis were included.
The dependence of comfort judgement on various meteorological factors was studied by using multivariate regression analysis.
p53 and bcl-2 remained as independent prognostic markers by Cox multivariate regression analysis.
Risk factors were assessed by a multivariate regression model.
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