Characteristics of the study participants by age groups (65–74 and ≥75 years) were compared using the chi-square test for categorical variables and the t-test for normal distributed continuous variables. The individual CVH components and the composite CVH metric scores were considered as the primary independent variables of interest. We employed binary and multinomial logistic regression models to examine the odds ratio (OR) and 95% confidence interval (CI) of dementia, AD or VaD (as dependent variables) associated with individual and composite CVH metrics (as independent variables) in the total sample. To explore the possible variations in the CVH metrics–dementia association, we examined the statistical interaction of CVH metrics with age groups (65–74 and ≥75 years). If a statistical interaction (p for interaction <0.05) or a marginally statistical interaction (0.05<p for interaction <0.10) was detected, we further performed stratifying analysis by age groups to assess the magnitude and direction of the interaction. In all the association analyses, we controlled for age (in years), sex, education, alcohol consumption, and APOE ε4 allele. Stata 14.0 for Windows (StataCorp LLC., College Station, TX, USA) was used for all analyses. Two-tailed p<0.05 was considered to be statistically significant.
Stata 14.0 for windows
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Stata 14.0 for Windows is a statistical software package developed by StataCorp. It provides a comprehensive set of tools for data management, analysis, and visualization. Stata 14.0 supports a wide range of statistical methods, including regression analysis, time series analysis, and survey data analysis.
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12 protocols using stata 14.0 for windows
1
Cardiovascular Health and Dementia Risk
2
Sociodemographic Determinants of Hypertension
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The distributions of normal pressure, prehypertension, and mild-moderate-severe (MMS) hypertension were assessed by sociodemographic characteristics. Using unaffordable expenses and services as covariates, logistic regression models were mainly used to assess the associations of hypertensive state, while adjusting for age, sex, marital status, current work status, and education. All analyses accounted for the RUMiC complex sample design by using STATA 14.0 for Windows (STATA Corp, College Station, TX, USA).
3
Epidemic Adenoviral Conjunctivitis Surveillance
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All sporadic and outbreak-related cases reported to RKI in the period of 1 January 2001 until 31 December 2015 according to the Protection against Infection Act were included (datasource: “SurvNet@RKI”, accessed 29.01.2016). All cases fulfilled one of the RKI reference definitions:
We also describe subgroups of cases with typing results reported to RKI in the period from November 2011 until December 2013 (epidemic period) as well as cases outside the epidemic period (2001 to October 2011, 2014 and 2015).
All statistical analyses were performed using STATA 14.0 for Windows (StataCorp LP, 4905 Lakeway Drive, College Station, TX 77845, USA).
Cases with the clinical picture defined as redness of the conjunctiva AND with laboratory confirmation by direct detection of pathogens in conjunctival swabs using antigen detection (e.g. ELISA, IFT), pathogen isolation (cultural) or nucleic acid detection by PCR.
Cases with the clinical picture defined as redness of the conjunctiva AND with an epidemiological link to a laboratory-proven infection in humans by human-to-human transmission or common source of exposure (e.g. ophthalmological examination devices). The incubation period of 5–12 days, occasionally longer, has to be taken into account.
We also describe subgroups of cases with typing results reported to RKI in the period from November 2011 until December 2013 (epidemic period) as well as cases outside the epidemic period (2001 to October 2011, 2014 and 2015).
All statistical analyses were performed using STATA 14.0 for Windows (StataCorp LP, 4905 Lakeway Drive, College Station, TX 77845, USA).
4
Survival Analysis of Treatment Outcomes
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The continuous variables were presented as an average ± standard deviation. Overall survival (OS) was assessed from the date of initiation of treatment until the date of death or the date at which patients were last known to be alive. The OS was analyzed using the Kaplan-Meier method, and the hypothesis tests were conducted using the log-rank test. Multivariable analyses were performed using the Cox proportional hazards model to evaluate the hazard ratio (HR). The prediction efficacy was validated using the area under the receiver operating characteristic curve (AUROC). All the statistical analyses were completed using two-sided tests, and a P value <0.05, or respective corrected value referring to the Bonferroni correction, indicated statistical significance. All the statistical analyses were performed using the software Stata 14.0 for Windows (StataCorp, TX, USA).
5
Assessing HCPs' COVID-19 Perceptions
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Data were analyzed using STATA 14.0 for Windows (College Station, TX, USA). The unit of analysis corresponded to a single center. Descriptive statistics were used to present demographic data and to evaluate the knowledge, attitudes, and perceptions of HCPs during the COVID-19 pandemic. Quantitative variables were described in the form of means and standard deviations (SD), and qualitative variables were described as numbers and percentages. The sum of some results exceeds the number of responses because of the option to answer as multiple choices. Some questions were open-ended and were analyzed using a coding technique, where similar answers are summarized by approximation into similar semantic content (14 ).
6
Comparing COVID-19 Outcomes in Treatment Groups
7
Comparative Diagnostic Accuracy of FNA and EUS
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It was reported that the accuracy of histological diagnosis by using a 22-gauge needle in 2 FNA procedures for pancreatic masses was 62.5%. Based on previous studies, the accuracy of histological diagnosis was estimated at 65% for the ProCore™ group. However, there is no relevant data regarding this aspect of EUS Sonopsy CY™. We estimated the accuracy of histological diagnosis at 85% for EUS Sonopsy CY™ based on our clinical experience. A 2-tailed sample-size calculation was performed with the type I error rate (α) set at .05 to attain 80% power to detect a difference of 20% in the accuracy of histological diagnosis. It resulted in target sample sizes of 82 patients for each method. Continuous variables pertaining to baseline characteristics were presented as the mean SD and range. The diagnostic accuracy was compared by using the McNemar test. All analyses were conducted using Stata 14.0™ for Windows (Stata Corp., TX, USA). A P value less than .05 was considered to indicate a statistically significant difference.
8
Genipin-Coated Suture Mechanical Properties
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As a result of the matched-pair design, mechanical data of uncoated and genipin-coated sutures were analyzed using ratio t-tests. According to D'Agostino's K-squared test [12 ], the ratios of treated to control for all mechanical variables were normally distributed. The significance level was set at 0.05 and the results are reported as medians and range if not stated otherwise. All significance tests were two-sided. One-way analysis of variance and t-tests were used to analyze differences in the level of absorption and fluorescence across groups. The longitudinal distribution pattern of fluorescence and absorption was analyzed by linear regressions using fluorescence as the dependent variable and distance from the suture as well as group as a dummy-coded independent variable. The regression coefficients reflect the difference in percent along the tendon and across groups. The statistical analyses and graphs were computed using MATLAB® (MATLAB® and Statistics ToolboxTM Release 2016b; MathWorks, Inc, Natick, MA, USA), GraphPad Prism® 7.02 for Windows (GraphPad Software, La Jolla CA, USA), or Stata® 14.0 for Windows (StataCorp LP, College Station, TX, USA).
9
Prognostic Significance of OLC1 in Gastric Cancer
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The chi-square test was performed to testify the correlation between the OLC1 expression and clinicopathologic parameters of gastric cancer. OS was used as the indicator of prognosis of gastric adenocarcinoma in our study. It was defined as the time from surgery until the date of death or the most recent follow-up. Overall survival curves were generated according to the Kaplan-Meier method, which verified by both the Wilcoxon test and the log-rank test. Cox's proportional hazard model was exerted for the univariate and multivariate analysis of prognostic values. Statistical significance was considered at P ≤ 0.05 two-tailed level throughout. All statistical analysis was accomplished by STATA 14.0 for Windows.
10
Viral Load and Clinical Outcomes
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Stata 14.0 for Windows (Stata Corp LP) was used for all the analysis described below. All quantitative variables followed a nonnormal distribution; they were represented as median and interquartile range (IQR), and the Mann–Whitney tests was used to assess significant differences. Qualitative variables were described as counts and proportions and χ2 or Fisher's exact test was used for comparisons.
ROC curve analyses were performed to estimate the best cut‐off point of baseline viremia for the composite endpoint of ICU admission and mortality during hospital admission (Poor outcome). A cut‐off value for RV was selected based on the best trade‐off between specificity and sensitivity.
Survival time and time to discharge were analysed by the Kaplan–Meier method. Differences in time to death or to discharge between different variables were analysed by log‐rank test.
ROC curve analyses were performed to estimate the best cut‐off point of baseline viremia for the composite endpoint of ICU admission and mortality during hospital admission (Poor outcome). A cut‐off value for RV was selected based on the best trade‐off between specificity and sensitivity.
Survival time and time to discharge were analysed by the Kaplan–Meier method. Differences in time to death or to discharge between different variables were analysed by log‐rank test.
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