Barely Significant
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“a numerical trend”

1,013 sentences · 1,013 papers · 1,817 search hits before verification · confirmed specimen

Sighted at

p=0.09

Listed by Hankins (2013) · Otte et al. (2022)

In the literature

The results for the two RWSs [ 1 , 33 ] with BCVA change measured at 6 months showed a numerical trend of higher improvement for aflibercept compared with ranibizumab, but the difference was not statistically significant ( n = 84, MD 5.09, 95% CI − 16.19 to 26.37, P = 0.64) (Fig. 2 ), similar to the subgroup of patients with non-ischemic CRVO (Supplementary Materials Table S10 ).
These changes showed a numerical trend towards benefit but were not statistically significant between high‐dose and placebo groups in either the mITT population ( P = 0.56) or ITT population ( P = 0.071) but certainly suggest a preservation of fat mass despite ongoing cachexia.
Tenecteplase showed a numerical trend toward benefit (OR 1.33; 95% CrI 0.98–1.78) but did not reach statistical significance.
ePoster.
Eur J Neurol · 2026 · PMC13310587
did not reach statistical significancep = 0.1593.2× alphaplainly factual
The Relationship Between the Severity of Cervical Inflammation and Cytological Abnormalities and HPV When examining the relationship between the severity of cervical inflammation, cellular abnormalities, and HPV carriage, a numerical trend toward higher rates of abnormal cytology with increasing inflammatory severity was observed; however, this association did not reach statistical significance ( p = 0.159).
Nonetheless, the T-VEC-pembrolizumab group showed a numerical trend toward longer median PFS (14.3 months vs. 8.5 months) and slightly higher ORR (48.6% vs. 41.3%) and DRR(42.2% vs. 34.1%), while maintaining a comparable safety profile [ 74 ].Although the combination of T-VEC and pembrolizumab did not achieve a significant overall survival advantage in the first-line setting for advanced melanoma, it remains under active investigation in PD-1 inhibitor-refractory melanoma and other malignancies.
Among people with obesity or overweight, a numerical trend of multiple obesity-related complications and higher mean (SD) CCI scores was observed in individuals with higher BMI than in those with lower BMI (CCI scores: normal BMI: 1.3 [1.8]; overweight: 1.1 [1.6]; Class 1 obesity: 1.2 [1.7]; Class 2 obesity: 1.3 [1.7]; and Class 3 obesity: 1.5 [1.8]).
There was a numerical trend of Eastern European-born patients of having the highest 5-year probability of LDKT [7.9% (95% CI: 5.9–10.3)], and of DDKT [65.7% (61.4–69.0)], and also the lowest rate of withdrawal from the WL [4.4% (2.8–6.5)]; of EU-born of having the highest rate of withdrawal from the WL [6.7% (6.3–7.0)]; and of non-European-born of having the lowest rates of LDKT [4.0% (3.2–5.0)] ( figure 1 and table 2 ).
Over the 3 phases, there was a numerical trend towards a slightly better accuracy from 23.6% to 28.0%—which was also observed within the two subgroups (residents and oncologists)—which however lacked statistical significance in all subgroups (see table 2 ).
The probability of achieving an ACR20 response, disease remission based on CDAI or SDAI, and Boolean remission were the only outcomes for which the effect of treatment was not statistically significantly different, although a numerical trend was observed in favour of CT-P13 SC.
Therefore, according to all models, contrary to what might be expected under the encoding variability hypothesis, there was at least a numerical trend for both old item variance and overall levels of old item strength to be greater in the fixed than variable condition, with no evidence that old item variance was greater in the variable condition.