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The interquartile range was calculated for each SNP and the 20 most varied were selected and analyzed separately. For each SNP, the patients were divided into two groups based on their gene copy number, with the median value as a cut-off. To compare the association between RAB6C and clinical characteristics, the Pearson χ² test was utilized.
Cumulative distant-recurrence risk was estimated using the Kaplan-Meier method. In the public data set, distant recurrence was calculated as previously described by Zhang et al (3 (link)). In the independent cohort, the end-point was defined as the first distant recurrence from the patient's primary breast tumor as described by Rutqvist and Johansson (15 (link),16 (link)). In this cohort, 3 patients died from breast cancer, but no date of distant recurrence was recorded. For these patients, the date of death was used as date of distant recurrence. Patients were censored at the last follow-up or at death due to causes other than breast cancer. Hazard ratios (HRs) and 95% confidence intervals (CIs) were estimated using Cox's proportional hazards model. P-values were obtained from two-sided Wald tests and the patients were followed up until 10 years after diagnosis. The microarray data was processed using R 2.14.1 and the statistical analyses were performed with Stata/SE 13.1 software.

Data from the AA Patient Satisfaction and Unmet Need Survey are presented overall for the 11 countries. In addition, participants were stratified according to sex (female, male), current age (≤40 years, 41–50 years, >50 years), duration of AA since diagnosis (<2 years, 2–4 years, >4 years), and current severity of scalp hair loss (<50%, 50–94%, ≥95%).
Data were analyzed descriptively. Continuous variables were described using mean and standard deviation (SD). Categorical variables were reported as the frequency and percentage within each category. No imputation of missing data was conducted. Analyses were performed using STATA/SE 13.1 software (StataCorp, College Station, TX 77845, USA).

Two types of biopsy outcome were tested in the study: PCa versus non-PCa and high-grade PCa versus everything else. The differences between the two types of biopsy outcome with respect to age, PV, tPSA, %fPSA, PSAD, p2PSA, %p2PSA, and PHI were assessed using the Student's t-test for normal data and the Wilcoxon rank sum test for skewed data. Due to nonnormal distributions of age, PV, tPSA, %fPSA, PSAD, p2PSA, %p2PSA, and PHI, these variables were log-transformed before any statistical analysis. The areas under the receiver operating characteristic (ROC) curves (AUC) of different variables (age, PV, tPSA, %fPSA, PSAD, p2PSA, %p2PSA, and PHI) were calculated in univariate regression analyses. AUC of PHI was compared separately with the AUC of age, tPSA, and %fPSA and multivariate analysis was used to assess the value of PHI in the diagnosis of PCa. The sensitivity and specificity of each variable were calculated to assess the diagnostic performance of the various assays in terms of PCa detection. All descriptive statistics and comparisons were performed using Stata/SE 13.0 software (StataCorp., College Station, TX, USA). A two-sided P < 0.05 was considered statistically significant in all of the analyses.

We used Stata/SE 13.0 software (StataCorp LP, College Station, TX, USA) and Review Manager, version 5.2.0 (Cochrane Collaboration, Oxford, UK) to perform data analysis. The I² and χ² tests were applied to assess heterogeneity. We considered I² values of 25%, 50%, and 75% as low, medium, and high levels of heterogeneity, respectively. Random-effects models were used for studies with significant heterogeneity, and fixed-effects models were used for studies without significant heterogeneity. The mean difference (MD) was used to evaluate the continuous outcomes, and relative risk (RR) was used for dichotomous data. A p value <0.05 was taken to indicate statistical significance.