“nearly significant”

The model included all significant ( p ≤ 0.05) or nearly significant ( p < 0.10) variables identified in the univariate analysis.

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.

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).

In case of significant or nearly significant overall tests ( p < 0.10), post-hoc tests were performed to identify pairwise differences.

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

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.

Developed rules included those covariables that showed significant or nearly significant association ( p < 0.10) with the dependent variable (ICU/death) in age/sex- and multivariable-adjusted logistic regression models.

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).

We also found that Genet (the family of the selfed and outcrossed seeds) has a significant (or nearly significant; 0.05 < p < 0.10) effect on many of our measures of vegetative vigor and reproductive output (Table 2 ).

Factors that were significant or nearly significant (p < 0.100) in the univariate analysis were included in the multivariate analysis.

We performed Tukey's post hoc tests in the package multcomp (Hothorn, Bretz, & Westfall, 2008 ) to separate means for significant ( p < 0.05) or nearly significant ( p < 0.1) herbivory treatment by patch type interaction effects.

Excess emergency room visits for coronary problems and otitis were nearly significant (p = 0.1).

Table 3 also reports the c parameter estimated for each group: the direct path was not significant in the Pre-Lockdown group, nearly significant in the Lockdown group ( p < 0.10), and significant in the After-Lockdown group ( p < 0.01).

We included all variables that were identified as significantly different (or nearly significant, p < 0.10); however, we did not identify any predictors for patients that required second-line neuroleptic therapy.

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 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).

All variables which turned out to be significant or nearly significant ( p < 0.1) determinants of ODD on the univariate analysis, were included in a single multivariate logistic regression model.

Dicamba drift exposure led to a nearly significant reduction in the total number of flowers produced over the experiment (treatment effect, χ 1 2 = 2.68, P = 0.10; Table S4 ) with S. spinosa, I. hederacea , and D. carota exhibiting significantly fewer flowers in the presence of dicamba (species × treatment effect, χ 8 2 = 6.18, P < 0.001; Table S4 , see Fig.

Notably, nearly significant differences (0.05 < p < 0.1) are indicated in the figures.

The difference in effect size is also nearly significant ( t = 1.302, p < .10).

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.

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.

Differences were considered significant at p ≤ 0.05 and nearly significant at p ≤ 0.1.

To avoid missing potentially relevant predictive factors, variables that were nearly significant ( p < 0.1) in univariate analyses were included in the multivariate analysis using forward LR selection to identify the independent predictors of 30-day mortality and a poor 6-month prognosis.

The Cox models were adjusted for significant or nearly significant ( p < 0.1) predictor for all-cause mortality in univariate Cox regression analysis including age, sex, diabetes mellitus, coronary artery disease, types of dialyzer membrane, serum hemoglobin, serum albumin, and serum TC.