Stata mp 12

The imaging feature data, demographic information, and vital status data were merged into a single file for subsequent statistical analyses using Stata/MP 12.1 (StataCorp LP, College Station, TX). Student’s t-test and ANOVA were used to test for differences in imaging features by the demographic features and imaging parameters. A correlation matrix was used to assess dependency between the imaging features. Survival analyses were performed using Cox proportional hazard regression and Kaplan-Meier survival curves; statistical significance was computed using the log-rank test. The imaging features were dichotomized into two groups using the median score value.

Distribution of the data was assessed though the Skewness/Kurtosis test for Normality. The characteristics of the sample were measured in number and percentages for categorical variables, and means and SD for normally distributed continuous data.
Since the FL score was normally distributed, crude scores were standardized into z-scores in order to identify children ranking below and over 1 SD and classify them into low FL level and high FL level, respectively. All data were analyzed by using STATA/MP 12.1 (StataCorp).

Data collected through written questionnaires were entered into a database created with EpiInfo 3.5.4 (Centers for Disease Control and Prevention, Atlanta, GA, USA). All the data were analyzed using the statistical software package Stata/MP 12.1 (StataCorp LP, College Station, TX, USA).
Absolute and relative frequencies were calculated for the categorical (qualitative) variables. The differences in the categorical variables for hesitancy and refusal and between before and after the intervention were analyzed using chi-squared tests (Mantel–Haenszel) and the McNemar test, respectively.
Correct responses to at least three of the five “sentinel” questions investigating HPV infection and vaccination knowledge was considered as a dependent variable (good knowledge) in the uni/multivariate analysis conducted with principal variables examined in the pre-intervention questionnaire.
Variables found to have a statistical association with a p-value ≤ 0.20 at the univariate analysis, to guarantee a more conservative approach, were included in the two multivariate backward stepwise logistic regression models carried out. The crude odds ratio (crude OR) and the adjusted OR (adj-OR) with 95% confidence intervals (CIs) were calculated in the logistic regression models. A p-value ≤ 0.05 was considered significant throughout the study.

Data were automatically collected on EU-Survey. The sample size was calculated assuming the percentage estimated of positive and not positive attitude towards vaccines; accordingly, 110 respondents would be needed. Absolute and relative frequencies were calculated for the categorical (qualitative) variables, and quantitative variables were summarized by their means and range. All variables found to have a statistically significant association with vaccination attitude in the univariate analysis were included in a multivariate backward stepwise logistic regression model. All variables with a p value ≤ 0.20 were selected in the multivariate model to guarantee a more conservative approach. A backward stepwise regression model was used. The crude odds ratio (crude OR) and the adjusted OR (AdjOR) with 95% confidence intervals (CIs) were also calculated in the logistic regression model. The level of significance chosen for the multivariate logistic regression analysis was 0.05 (2-tailed). We entered all the information into a database created with Epi Info™ 3.5.4 (Centers for Disease Control and Prevention, Atlanta, GA, USA). All the data were analyzed using the statistical software package Stata/MP 12.1 (StataCorp LP, College Station, TX, USA).

Absolute and relative frequencies were calculated for the categorical (qualitative) variables and normally distributed quantitative variables were summarized by their means (standard deviations). The differences in the categorical variables were analyzed using chi-squared tests (or Fisher’s exact test when appropriate) and the Student t-test for the means.
All the variables that were found to have an association with protective Hepatitis B surface antibody titers (≥10 mIU/mL) at the first visit were included in a multivariate backward stepwise logistic regression model. Crude and adjusted OR with 95% confidence intervals (CIs) were also calculated in the logistic regression model. All information were entered into a database created with Excel 10.0. All data were analyzed using the statistical software package Stata/MP 12.1 (StataCorp LP, College Station, USA).

Pearson’s chi-square was used to test for differences in categorical patient characteristics across the case cohorts and ANOVA, Van Der Waerden normal scores test, and Wilcoxon rank-sum test were used test for differences in continuous variables. Survival analyses were performed using Kaplan-Meier survival curves, the log-rank statistic, and multivariable Cox proportional hazard models. Progression-free survival (PFS) and overall survival (OS) were assessed from date of lung cancer diagnosis to the date of an event or date of last follow-up. For PFS an event was defined as death or progression of cancer; for OS an event was defined as death. Among individuals without an event, censoring occurred at either 5-years or date of last follow-up if less than 5-years. Overall and lung cancer-specific death rates per 1,000 person-years were also calculated. All statistical analyses were two-sided and performed using SAS version 9.3 and Stata/MP 12.1 for Windows (32-bit).