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“showed a trend”

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showed a trendp <0.204.0× alpha
Other variables that showed a trend (i.e., p <0.20) included age (higher age was associated with greater anxiety), educational level (persons with college education had greater anxiety than those with elementary education), female gender (women were more anxious than men), and the presence of medical complications (those with complications had less severe anxiety).
showed a trendp < 0.24.0× alpha
Starting with all variables that showed a trend towards an association ( p < 0.2), a best subset regression procedure was used to identify the most suitable and parsimonious multivariate model, defined as the one with the lowest Akaike Information Criterion (AIC), a widely recognized measure of model fit [ 43 ].
showed a trendp = 0.24.0× alpha
Under conditions of endotoxemia, the levels of membrane-bound Gal3 statistically decreased in all the immune cell subsets with the exception of CD8 + T-cells, CD4 + T-cells, and DCs, which only showed a trend ( p = 0.2, p = 0.056 and p = 0.056, respectively) to a reduction of levels of Gal3 after the LPS exposure ( Figure 2 ).
showed a trendp ≤ 0.24.0× alpha
Firstly, in the exploratory MARS cohort 4 out of the 16 candidate genes (from 18 features), showed significantly different expressions ( p ≤ 0.05) between the response groups at least under one of the incubation conditions: glutamate decarboxylase 1 ( GAD1 ), FYN binding protein ( FYB ), receptor activity modifying protein 1 ( RAMP1 ), TBC1 domain family member 9 ( TBC1D9 ), while other four genes showed a trend ( p ≤ 0.2): paired like homeodomain 1 ( PITX1 ), nuclear factor I B ( NFIB ), glutamate ionotropic receptor NMDA type subunit 2A ( GRIN2A ), aminoadipate aminotransferase ( AADAT ) (Fig. 2b, c , Supplementary Table 8 ).
showed a trendp < 0.24.0× alpha
Starting with all variables that showed a trend towards an association ( p < 0.2), a best subset regression procedure was used to identify the most suitable and parsimonious multivariate model, i.e., the one with the lowest Akaike information criterion, which is a well-known parameter of the goodness of fit of the model ( Akaike, 1992 ).