“nearly significant”

Factors that were significant and nearly significant in univariate analysis ( P < 0.1) were included in multivariate analysis.

This difference in proportion of males between wet-comb group and mousse-treated group was statistically significant (p < 0.05) and the difference between wet-comb group and the lotion-treated group was nearly significant (0.05 < p < 0.1).

IL-12 levels were higher in those with the syndrome ( p < 0.10), and the increases were nearly significant for IL-1β ( p < 0.10), IL-6 ( p < 0.10), and IL-10 ( p- value > 0.05).

Then, the variables that were found to be significant or nearly significant ( p < 0.1) in univariate logistic regression analyses were included in a backward-stepwise multivariate logistic regression analysis.

DHA-supplemented HS males did show a nearly significant decrease in duration (M = 30 s, SE = 5.03, p = 0.10) compared to control-fed HS males and spent the least total time grooming than all other mice ( Figure 3 A).

All independent variables that were significant or nearly significant in univariable analysis ( P < 0.1) were included in the multivariable model.

For the first research question, only variables, which were significant or nearly significant ( p < 0.1) in bivariate analysis, were entered into a regression analysis with the main primary outcome complications.

The difference between group B and group D is significant on day 1 (p <0.05) and nearly significant on day 7 and day 14 (p < 0.1).

The variables that showed significant, or nearly significant ( p < 0.1), relationships in univariate analyses were included in the final logistic regression models, and adjusted odds ratios (ORs) were generated for 2 outcomes: availability of any results and availability of peer-reviewed results.

Multivariate analysis of significant or nearly significant ( P < 0.1) correlations between maternal characteristics (such as age over 35 years, primiparity, marital status, chronic illnesses, and smoking during pregnancy) and the incidence of obstetric outcomes was based on multiple logistic regression analysis (SAS, Institute Inc., Cary, NC, USA, version 9.1).

With one exception (local dynamic stability or logarithmic divergence rate in mediolateral direction), all differences in significant associations were still nearly significant with P <.10.

The covariates, which were found to be significant or nearly significant (p≤0.10) in univariate analysis, were included to construct a multivariate model for assessing determinants of treatment failure by forward stepwise logistic regression.

Multiple logistic regression using the stepwise forward method was used to evaluate the independent risk factors by including all the significant and nearly significant parameters ( p <0.1) The results of the logistic regression analysis are reported as odds ratios (OR) with 95% confidence intervals (CI). p -values less than 0.05 were considered statistically significant.

Those variables that were significant or nearly significant (i.e., p <.10, two-tailed test) in the initial ANOVAs presented above were used in subsequent analyses.

Those factors with a significant ( p < 0.05), or nearly significant ( p < 0.1) difference between groups at baseline, as well as those with a significant association with mortality in the univariate analysis, were stepwise entered in a multivariate Cox model.

Mutual adjustment were then applied including all significant or nearly significant factors (p<0.1) with additional adjustment for gender, age and current smoking.

As mentioned, the variables that had a significant or nearly significant relationship with sex outside the marriage in the univariate analysis (P < 0.1) were modeled using logistic regression.

The comparison of GFP + and no GFP regions in individual organoids revealed two main types of optical sections: the group including highly significant (independent t -test, p < 0.05) and nearly significant (independent t -test, p < 0.1) statistical differences in oxygenation between GFP + and no GFP and the group with no statistical differences (p > 0.1).

In order to determine prognostic factors for survival, a Cox regression model was created with variables that were statistically significant or nearly significant ( P < .1) in univariate analysis.

By contrast, a nearly significant (p = 0.10) reduction in rCBF (↓19%) was present in EMF-treated Tg mice during their ON period vs.

As indicated by the higher likelihood scores of model M2a ( Table S3 ), these tests provide significant evidence of adaptive evolution within ESAGs 4, 5, 6, and 7, and are nearly significant (p<0.1) for ESAG1 and ESAG3b .

This is supported by the significant ( P < 0.05) or nearly significant ( P < 0.10) differences from 100 (i.e., similar efficacy) in the RBA of DL-Met to L-Met shown by both the linear and exponential models for ADG and FCR.

There was a nearly significant difference in the type of gene mutation observed between the non-progression and PGs (P<0.1).

Metabolites found significant (p-value ≤ 0.05) and nearly significant (p-value ≤ 0.10) according to a student t-test are included.

We also observed smaller, but nearly significant changes ( p < 0.10) in the FACT-G total score (diff = -11.02) and FACT-G functional well-being score (diff = -5.21).