“marginally significant”

For markers with at least marginally significant interaction with treatment ( p <0.20), the analysis was performed for each treatment group separately, as well as for all subjects together and adjusted for treatment.

In addition, the translation pathway was marginally significant with adj. p = .20 (Figure 3C and Table S5C ).

Variables in the multivariate logistic analysis were selected based on variables which were marginally significant with p <0.20 in univariate logistic analysis.

In the sequence, correlates that indicated at least marginally significant associations ( p ≤ 0.20) in the bivariate analysis were selected to be included in hierarchical multiple regression procedures.

Variables in bivariate analysis, that were marginally significant with P < .20, were selected for multivariable analysis.

Any biomarker with a marginally significant p value ( p < 0.20) for either a main effect or interaction will be eligible for the risk stratification model and continued in the Validation Phase.

The main effect of Subscale (ownership M = 3.56; SD = 1.48; location M = 4.04; SD = 1.49; agency M = 3.49; SD = 1.85) [F(2, 56) = 3.13; p = 0.051; η ρ 2 = 0.1) was marginally significant, but not the main effect of Condition (synchronous M = 3.86; SD = 1.53; asynchronous M = 3.54; SD = 1.71) [F(1, 28) = 1.71; p = 0.2; η ρ 2 = 0.05).

Significant and marginally significant linear and polynomial regressions are shown for each enzyme and each soil characteristic, P <0.2.

Each explanatory variable was first screened for its unconditional association with the outcome variable; variables that were marginally significant (p<0.2) or considered confounding were retained for inclusion in the multivariable regression model.

Factors found to be significant ( P < 0.05) or marginally significant ( P < 0.20) in univariate analysis were included in multivariate analysis using a forward stepwise method to explore the independent effects of different variables on the incidence of BSI.

The variables identified as significantly associated with outcome variables ( p < 0.05), that were marginally significant ( p < 0.20, t ) in the univariate analysis, or that are considered clinically relevant were selected for inclusion in the Cox regression model, which analyzed parameters associated with a loss in kidney graft function equal to at least 10% of the eGFR over the follow-up period of the 148 patients (median duration of follow-up: 13.3 months, 25–75th percentile: 12.1–15.1 months post-evaluation).

Variables in the multivariate analysis were selected on the basis of variables, which were marginally significant with P <0.20 in the univariate analysis.

Two-sided p-values were defined statistically significant when P < 0.05, and marginally significant when 0.05 < P < 0.2.

In order to adjust for confounders and to identify the independent predictors associated with the mean scores of subscales, independent variables that were marginally significant ( P < 0.20) in bivariate analysis were included in a multivariate linear regression analysis.

Anxiety showed marginally significant correlation with both SBP and DBP ( r = 0.13, p = 0.20; r = 0.15, p = 0.13).

Variables in the multivariate analysis were selected based on variables which were marginally significant with P<0.20 in univariate analysis.

In addition, based on the criteria we set earlier that only those variables that were determined to be significant ( p <0.05) or marginally significant ( p <0.2) in the bivariate analyses were included in the subsequent path analysis, functional health status, helping others, and the use of community services were also included for further testing in the proposed path model.

Multivariate logistic regression was performed using a backward elimination approach, including marginally significant variables from the univariate analyses ( p < 0.20) and adjusting for key risk factors highlighted in current published guidelines including age, Aboriginal status, and men with same-sex partners [ 9 , 24 ].

The variables found to significantly associate ( p< 0.05) with outcomes variables or marginally significant ( p< 0.20) in the univariate analysis, or that had a clinical relevance were selected in the multivariate model.

After checking for collinearity with a correlation matrix, variables that were marginally significant with a P -value<0.2 were included in the multiple linear regression models.

A marginally significant positive correlation between age and IHP was observed ( r = .2, P = .2).

If models had marginally significant interactions (0.05 < P < 0.2), the two main effects were interpreted both in a new two-way model with the interaction removed and in two separate one-way models after splitting the data.

Logistic regression performed with early death as the dependent variable on significant ( p < 0.05) or marginally significant ( p < 0.2) variables in the univariate analysis found Penn-nonAa status (OR = 7.10, 95% CI = 2.50–20.15, p < 0.01), patient age (OR = 1.05 per year, 95% CI = 1.00–1.11, p = 0.02) and not using the innominate artery for arterial inflow (OR = 6.04, 95% CI = 1.46–24.84, p = 0.01) as independent predictors of early death ( Table 3 ).

Marginally significant variables with p < 0.20 in univariate analysis were included in multivariate analysis.

All statistically marginally significant prognostic factors identified by univariate analysis ( P < 0.2) (sex, age, limitation in positional changes state, GCS score) were entered into a Cox proportional hazard (PH) model with forward stepwise (likelihood ratio) to identify independent predictors of VAP event.