“near-significant”

The difference in the rates of conversion to ADD between ApoE ε4 carriers and ApoE ε4 non-carriers were near-significant ( p = .123; one-tailed chi-square test).

No significant differences were found based on managerial role, except for a near-significant difference in professional development scores among managers (M = 5.26 vs. 4.68, p = 0.123).

GTAA-ET), a near-significant difference was observed only in the anterolateral chest region (pain: p = 0.130, β = −0.100, 95% CI: −0.229 to 0.029; numbness: p = 0.051, β = −0.162, 95% CI: −0.326 to 0.001), with the GTSA-ET group exhibiting lower pain and numbness scores than the GTAA-ET group.

At 6 weeks postoperatively, a healed hip capsule (with no apparent capsulotomy defect) was observed in 10 (43.4%) hips that underwent capsular repair and 4 (15.4%) hips that did not undergo capsular repair, a difference that was near-significant [ χ 2 (1) = 2.29, P = 0.13].

In both the LATL and the RATL, NonRestr-Adj (LATL: 7.25 ± .77 nAm, RATL: 7.74 ± .89 nAm) elicited larger amplitudes in both hemispheres than Restr-Adj (LATL: 5.33 ± .58 nAm, RATL: 5.76 ± .66 nAm), but not significantly so (although a potentially revealing near-significant cluster was observed in the LATL from 69 ms to 135 ms, p = .15).

A near-significant increase in CCT was observed in the > 10-year group (511.7 vs. 501.3 μm, p=0.163), suggesting that prolonged diabetes may affect corneal thickness.

Our binary split at 40 years cannot capture nonlinear age trajectories, and the near-significant left-side width trend ( p = 0.18) suggests that age effects are dimension-specific.

Other viral pathogens , including SARS-CoV-2, adenovirus , and other viruses, showed similar or slightly higher rates in the mild group, with other viruses reaching a near-significant difference [3 (3.4%) in severe vs. 15 (6.9%) in mild, p = 0.198].

Firstly, bivariate regression models were run to identify significant ( P < = 0.05) or near-significant ( P < 0.20) associations between each predictor and job satisfaction.

Variables included in the multivariate analysis were either those identified as significant ( p < 0.05) or near-significant ( p < 0.2) in the univariate analysis, known contributory factors documented in existing literature, or existed as baseline discrepancies between groups.

There were 21 near-significant ( p < 0.2, Supplementary Data 14 ) genes selected for training of the Elastic regularized Cox regression model with 61 patients from the Beijing site.

r medication, dyslipidemia or medication, diabetes or medication, fasting blood glucose, HbA1c ≥ 6.5%, eGFR < 60 mL/min/1.73 m 2 , uric acid ≥ 7.1 mg/dl, metabolic syndrome, smoking history and alcohol consumption by ≥ 20 g/day significantly associated yet in multivariate analysis of significant or near-significant (p < 0.2) covariates of univariate analysis only dyslipidemia (OR:1.74) and fasting glucose (1.01) along with aging per year (OR:1.10) and male gender (OR:2.46) remained significant with CACs > 100.

A similar analysis restricted to arterial events occurring before or after diagnosis revealed a near-significant association for driver mutation profile ( p = 0.2; JAK2 22%, CALR 14%, MPL 22%, and TN 19%) and significant associations for the absence of ASXL1 ( p = 0.02) or RUNX1 ( p = 0.05) mutations; a similar analysis for venous events marked driver mutation profile ( p = 0.03; JAK2 21%, CALR 10%, MPL 15% and TN 13%) and absence of SRSF2 ( p = 0.03) or EZH2 ( p = 0.02) mutations as being significant.

Multivariable models all included age and gender, with the abovementioned potential confounders also considered in the model where the differences between the groups were near-significant ( P < 0.20).

Variables that showed significant or near-significant effects in univariate analyses ( p < 0.2) were included in the multivariate regression models to adjust for potential confounders.

Variables that showed a significant or near-significant association ( P < .20) with the dependent variable were included in the multivariate model, based on the authors’ previous analytical experience.

Significant ( P < .05) or near-significant variables ( P < .2) identified in the univariate analysis were further entered into the multivariate logistic regression model to determine the adjusted results.

Variables with a statistically significant or near-significant association (p<0.2) in univariable models were included in multivariable analysis.

Using a Cox proportional hazards model, we also assessed the prognostic value, for RFS, of parameters that were significant or near-significant (P < 0.2) in univariate analysis, i.e.

Further investigation of the near-significant effect of sex on SA rate, revealed that females perform at chance level [0.499 ± 0.092, t (1,14) = –0.061, p > 0.20], while males show SA rates significantly below chance [0.442 ± 0.094, t (1,19) = 2.754, p = 0.013, p < BH critical value: 0.014].

Variables included in the multivariate analysis were those identified as either significant ( p < 0.05) or near-significant ( p < 0.2) in the univariate analysis, known contributory factors documented in existing literature, or those that existed as baseline discrepancies between groups.

Significant or near-significant variables in the univariate analysis ( P < 0.2) and cohorts study based (early-onset versus late-onset) were included in the multivariate analysis.

Possible predictors of functional outcome were identified using a univariable linear regression, subsequently adding all variables with a significant or near-significant relationship with the 12-month OMAS (p < 0.2) to multivariable linear regression in order to identify independent predictors of functional outcome.

Variables to include in SEMs were guided by the significant ( P < 0.05) and near-significant ( P < 0.2) outcomes of the binomial regression models described by Beatty et al.

Although these trends did not reach statistical significance, Kruskal-Wallis tests revealed a near-significant difference in NC across periodontal stages (H = 4.60, p = 0.204).