“…In patients with CM, 3 to 16% more GMB‐treated patients (N = 507) had improvements in the 14 MSQ items compared with PBO (N = 494), though differences were statistically significant in only 19 of 28 comparisons. …”
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“…The samples were assayed using commercially available ELISAs. Statistical analyses were performed using non-parametric Kruskal–Wallis tests adjusted for multiple comparisons, and multiple logistic regression. …”
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“…All analyses were carried out by STATA 15 statistical package. MAIN FINDINGS: 379 COVID-19 patients [273 (72% were male; mean age was 61.67 (SD 15.60)] were enrolled and 1,805 measures per parameter were analysed. …”
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“…The study used Organisation for Economic Co-operation and Development data, CONCORD-3 cancer data, and Medicare data to compare their outcomes with all residents in 12 other developed countries: Australia, Austria, Canada, Denmark, Finland, France, Germany, Japan, the Netherlands, Norway, Sweden, and Switzerland. Statistical analysis took place from July 25, 2017, to August 29, 2020. …”
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“…Differences in rates of valve procedures, mechanical ventilation, length of stay, and inpatient costs were not statistically significant (Figure). Regression-adjusted Outcomes [Image: see text] CONCLUSION: Compared with other reproductive aged female IE patients, patients with maIE are younger, healthier, more likely insured by Medicaid, and report higher rates of drug abuse. …”
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“…A similar trend (not statistically significant) was observed in the ZOE-50 (VE: 26.9%; p-value: 0.4318) and ZOE-70 (VE: 28.4%; p-value: 0.1877) studies. …”
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“…[Image: see text] CONCLUSION: People initiating ART during AHI gained weight over 48 weeks, with persons taking INSTIs gaining more weight, though this finding did not reach statistical significance due to small sample size. Amongst INSTI-treated persons, those not on EVG gained more weight than those on EVG. …”
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“…Differently from a statistical modelling, a numerical method is a type of mathematical modelling that is not dependent on a probability distribution. …”
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“…*=differences in means using paired t-test were statistically significant with p<0.05. HCV= Hepatitis C Virus, SVR= Sustained Virologic Response TC=Total Cholesterol, HDL= High Density Lipoprotein, LDL= Lipoprotein, TG= Triglycerides. …”
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“…Pathway analysis did not result in significant enrichment, suggesting identification of novel biology (Fig1). Statistical deconvolution of bulk transcriptomic data found M1 macrophages, plasmacytoid dendritic cells, CD14+ monocytes, CD4+ T cells and total B cells changed in the same direction across COVID-19 and non-COVID-19 infections. …”
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“…This model had good discrimination (C-statistic 0.874; 95% CI 0.839-0.908) and calibration performance (Figure 2). …”
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“…Student’s t test or one-way ANOVA was used for statistical analysis. RESULTS: HI insult significantly upregulated miR-210, downregulated TET2 protein abundance, and increased NF-κB subunit p65 acetylation level and its DNA binding capacity to the interleukin 1 beta (IL-1β) promoter in the brain of mouse pups. …”
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“…Male circumcision coverage among all men (PRR: 1.05, 95% CI: 0.96 to 1.14, p = 0.31) and HIV viral suppression among HIV–positive participants (PRR: 1.04, 95% CI: 0.98 to 1.12, p = 0.20) were higher in the intervention arm, but differences were not statistically significant. No differences were seen in secondary outcomes. …”
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“…These patients were subjected to a periodontal examination using the above parameters and classification methods. The following statistical tests were implemented: Shapiro–Wilk test, Levene's test, Mann Whitney's U analysis, Univariate Analysis of Variance (ANOVA); the post-hoc analysis was performed with the use of Tukey's honest significant difference test (HSD), Kruskal–Wallis's non-parametric test, Spearman's rank correlation, logistic regression analysis, linear regression analysis and ROC analysis. …”
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“…The ggpubr package was used to perform statistical analyses with Student’s t-test. RESULTS: A total of 275 DEGs were identified between 16 RA samples and 10 OA samples from the datasets GSE77298 and GSE82107. …”
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“…DESIGN, SETTING, AND PARTICIPANTS: A large population-based cohort study was designed to identify the risk of SGMNs in patients with rectal cancer diagnosed from January 1973 to December 2015. The statistical analysis was conducted from September 2019 to April 2020. …”
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“…The number of hospitals stays and 28-day-motality in the positive mNGS group were significantly higher than those in the negative group, and the difference was statistically significant (P < 0.05). Age was significant in multivariate logistic analyses of positive results of mNGS. …”
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“…The exposure-response curves between each pollutant and the log-relative risk of COPD-related mortality exhibited linear relationships. Statistically significant differences in the associations between pollutants and COPD-related mortality were not observed among sociodemographic factors including age, gender, and marital status. …”
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“…The percentage of AKI in patients aged 50 years or older was 52.9% (46/87), which was significantly higher than that [22.5% (63/280)] in patients under 50 years (χ(2) = 6.347, P = 0.013). We observed statistically significant differences in age (43 [27, 59] years vs. 28 [20, 44] years, Z = 5.487, P < 0.001), male (78.0% vs. 51.4%, χ(2) = 22.470, P < 0.001), serum albumin (19.9 ± 6.1 g/L vs. 21.5 ± 5.7 g/L, t = 2.376, P = 0.018), serum creatinine (129.5 [105.7, 171.1] μmol/L vs. 69.7 [57.7, 81.9] μmol/L, Z = 14.190, P < 0.001), serum urea (10.1 [6.2, 15.8] mmol/L vs. 4.7 [3.6, 6.4] mmol/L, Z = 10.545, P < 0.001), IgE (266.0 [86.7, 963.0] IU/ml vs. 142.0 [35.3, 516.5] IU/ml, Z = 2.742, P = 0.007), history of diabetes (6.4% vs. 1.2%, P = 0.009), and history of hypertension (23.9% vs. 5.1%, χ(2) = 28.238, P < 0.001) between the AKI group and the non-AKI group. …”
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“…For the spatial analysis, Arc-GIS version 10.6 was used to explore the spatial distribution of women health care decision making and spatial scan statistical analysis to identify hotspot areas. Considering the hierarchical nature of EDHS data, a generalized linear mixed-effect model (mixed-effect logistic regression) was fitted to identify significant determinants of women’s health care decision making autonomy. …”
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