Stata statistical software 12 se

Genotype data were checked for deviation from Hardy-Weinberg equilibrium (HWE) by chi-square test.
Both genotype and allele frequencies of MUC5B rs35705950 were calculated and compared between ASSD-ILD+ patients and healthy controls, patients with ASSD stratified according to specific clinical features of the disease (presence/absence of anti Jo-1 antibodies or ILD), ASSD-ILD+ patients stratified according to the presence of an UIP and non-UIP HRCT pattern, as well as between ASSD-ILD+ patients and those with ILD unrelated to ASSD.
To test for association, 3 × 2 and 2 × 2 contingency tables as well as chi-square test and/or Fisher´s exact test, when appropriate, were used. Strength of associations were estimated using odds ratios and 95% confidence intervals. P-values lower than 0.05 were considered as statistically significant.
All analyses were performed with STATA statistical software 12/SE (Stata Corp., College Station, TX, USA).

Data were expressed as mean ± standard deviation (SD) for continuous variables, and number of individuals (n) and percentage (%) for categorical variables. The differences between RA-ILD⁺ and RA-ILD^- patients in sex, smoking status, RF and ACPA status, as well as in therapies received were analysed by chi-square test, whereas the differences in age at study, duration of RA disease, CRP and ESR levels and PFTs were determined by Student´s t-test. Comparisons of EPC frequency between two study groups were performed by Student’s t-test. Relationship of EPC frequency with continuous variables and categorical variables related to disease features was carried out via estimation of the Pearson’s correlation coefficient (r) and one-way ANOVA, respectively. p-values <0.05 were considered as statistically significant. Statistical analysis was performed using STATA statistical software 12/SE (Stata Corp., College Station, TX, USA).

All genotype data were checked for deviation from Hardy-Weinberg equilibrium (HWE) using http://ihg.gsf.de/cgi-bin/hw/hwa1.pl.
cIMT values were displayed as mean ± standard deviation (SD). The association between genotypes and alleles frequencies of the IL33 rs3939286, IL33 rs7025417, IL33 rs7044343, IL1RL1 rs2058660, IL1RL1 rs2310173 and IL1RL1 rs13015714 polymorphisms and cIMT values was tested using unpaired t test to compare between 2 groups and one-way analysis of variance (ANOVA) to compare among more than two groups. Comparisons of means were adjusted for sex, age at the time of US study, follow-up time and center as potential confounders using analysis of covariance (ANCOVA). Statistical significance was defined as p<0.05. All analyses were performed with STATA statistical software 12/SE (Stata Corp., College Station, TX, USA).

All genotype data were checked for deviation from Hardy-Weinberg equilibrium (HWE) using http://ihg.gsf.de/cgi-bin/hw/hwa1.pl.
cIMT values are displayed as mean and standard deviation (SD). The association between genotypes and alleles of each polymorphism and cIMT values was tested using unpaired t-test to compare between 2 groups and one-way analysis of variance (ANOVA) to compare among more than two groups. Comparisons of means was adjusted for sex, age at the time of US study, follow-up time and traditional CV risk factors (hypertension, diabetes mellitus, dyslipidemia, obesity, and smoking habit) as potential confounders using analysis of covariance (ANCOVA).
Differences in the genotypic and allelic frequencies of each polymorphism according to the presence/absence of carotid plaques and CV events were calculated by χ² or Fisher tests when necessary (expected values below 5). Strength of associations were estimated using odds ratios (OR) and 95% confidence intervals (CI). Results were adjusted for sex, age at the time of US study, and traditional CV risk factors (hypertension, diabetes mellitus, dyslipidemia, obesity, and smoking habit) by logistic regression.
Statistical significance was defined as P < 0.05. All analyses were performed with STATA statistical software 12/SE (Stata Corp., College Station, TX, USA).

Continuous data are described as mean and standard deviation (mean ± SD) and categorical variables as percentages.
The strength of association between HSP and HLA-B phenotypes was estimated using odds ratios (OR) and 95% confidence intervals (CI). Levels of significance were determined using contingency tables by either the chi-square test or Fisher exact (expected values below 5) analysis. Results were adjusted for Bonferroni correction. To obtain an internal validation, we carried out a bootstrap test with 1,000 replications.
All analyses were performed with STATA statistical software 12/SE (Stata Corp., College Station, TX, USA).
The linkage disequilibrium between HLA alleles was calculated using the PLINK software [13 (link)].

Data were reported as the number of individuals (n) and percentage (%) for categorical variables and mean ± SD for continuous variables. Differences in ET-1 serum levels were compared between the study groups by ANOVA, adjusting p values for sex, age and smoking history. Number of packs of cigarettes per year was also included as a potential confounding factor when differences between IPF and AD-ILD patients were evaluated. When significant differences between groups were obtained, ROC analysis and the optimal cut-off value (the higher value obtained from the formula sensitivity% + specificity% − 1) of ET-1 for the diagnosis of ILD was performed. In addition, the association between ET-1 levels and clinical characteristics of all the patients were calculated by ANOVA or Pearson’s partial correlation coefficient (r), when appropriate, adjusting p values for the potential confounding factors previously mentioned. p-values ≤ 0.05 were considered statistically significant. Statistical analysis was carried out with STATA statistical software 12/SE (Stata Corp., College Station, TX, USA).

Continuous variables were expressed as mean ± standard deviation (SD) and categorical variables as number of individuals (n) and percentage (%).
Analysis of variance (ANOVA) was used for the comparison of protein levels between the two study groups, adjusting for the following potential confounding factors: sex, age at the time of the study, and smoking history. When significant differences between groups were obtained, receiver operating characteristic (ROC) analysis was performed. The area under the curve (AUC) with a 95% confidence interval (CI) was calculated. The optimal cutoff values of E-selectin, ICAM-1, and ET-1 for discriminating AD-ILD+ from AD-ILD− were calculated by the Youden index (the higher value obtained from the formula sensitivity% + specificity%—100).
The association of protein levels with continuous and categorical variables was analyzed via the estimation of Pearson’s partial correlation coefficient (r) and linear regression, respectively, adjusting for the above-mentioned potential confounding factors.
Statistically significant differences were considered as p < 0.05. Statistical analysis was performed using STATA statistical software 12/SE (Stata Corp., College Station, TX, USA).