During the statistical analysis, we first described the demographic characteristics and comorbidities of the study patients. Student t-test was used to estimate the mean difference of the continuous variables, and Pearson Chi-square was applied to the categorical variables. The incidence rate of dementia (AD or PD) was evaluated as the number of the patients with COPD and dementia divided by the total person-years, yielding rates per 10,000 person-years of observation. The incidence rate ratios (IRRs) of dementia were estimated under Poisson regression. Cox proportional hazard regression model with adjustment for potential confounders was used to assess the risk of dementia. The potential confounders included age, gender, urbanization, coronary artery disease, stroke, hyperlipidemia, hypertension, diabetes, and head injury. Subjects who did not present AD or PD before the end of study period (ie, December 31, 2011) were considered censored. We also performed Kaplan-Meier analysis to compare the cumulative incidence of dementia (AD or PD) in the patients with COPD and those without COPD. A P value of <0.05 was considered statistically significant. All statistical analyses were performed using the Statistical Analysis Software (SAS) System, version 9.3 (SAS Institute Inc., Cary, NC).
Sas system version 9
Manufactured by SAS Institute
Sourced in United States, Japan
SAS System version 9.4 is an integrated software suite designed for advanced analytics, data management, and business intelligence. It provides a comprehensive set of tools and capabilities for data analysis, reporting, and modeling. The core function of the SAS System is to enable users to access, manipulate, analyze, and report on data from various sources, allowing them to gain insights and make informed decisions.
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Lab products found in correlation
Prism 6, by GraphPad (4 mentions)
Spss version 22, by IBM (2 mentions)
Spss version 24, by IBM (1 mentions)
Prism 8, by GraphPad (1 mentions)
Spss 25 statistical software, by IBM (1 mentions)
Phoenix winnonlin v8, by Certara (1 mentions)
Stata software, by StataCorp (1 mentions)
Spss statistics 22, by IBM (1 mentions)
113 protocols using sas system version 9
1
Epidemiological Study of COPD and Dementia
2
Randomized, Placebo-Controlled Trial
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The randomization module of SAS® system version 9.3 (SAS Institute, Cary, NC, USA) was used to create a random assignment table using a sequence of random A and B numbers before the beginning of the trial. Registered subjects were randomly assigned in a 1:1 ratio to either the BRE or placebo groups before their first visit. According to the randomization list, test product tablets were assigned a randomization number, and the list was kept confidential throughout the study.
3
Multilevel Modeling of Neighborhood Socioeconomic Disparities
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Chi-square tests were used to examine potential differences in individual characteristics across quartiles of the neighborhood SED index. We applied a generalized linear mixed modeling approach to fit the multilevel logistic regression to quantify the geographic variation in and the association of neighborhood SED with MSP. The trend of SED association was tested by using the medians of each SED quartile in the models.
To examine if groups of individual covariates could explain potential geographic variation and the role of neighborhood SED, we fit four multivariate models adjusting for any one or all three groups of individual characteristics. Geographic variation in MSP was quantified using census tract-level variance from the fitted multilevel model. Since this variance has no meaningful unit and is hard to interpret by itself, we computed two heterogeneity measures, median odds ratio (MOR) and interquartile odds ratio (IqOR) to estimate the geographic heterogeneity in MSP based on commonly used odds ratios [39 (link), 40 (link)]. Calculation of these two measures is described elsewhere [39 (link), 40 (link)]. The fit of the multilevel models was based on the scaled deviance, with lower deviance indicating better fit [41 ]. The data management and statistical analysis were conducted in SAS System (version 9.3, SAS Institute Inc., Cary, North Carolina).
To examine if groups of individual covariates could explain potential geographic variation and the role of neighborhood SED, we fit four multivariate models adjusting for any one or all three groups of individual characteristics. Geographic variation in MSP was quantified using census tract-level variance from the fitted multilevel model. Since this variance has no meaningful unit and is hard to interpret by itself, we computed two heterogeneity measures, median odds ratio (MOR) and interquartile odds ratio (IqOR) to estimate the geographic heterogeneity in MSP based on commonly used odds ratios [39 (link), 40 (link)]. Calculation of these two measures is described elsewhere [39 (link), 40 (link)]. The fit of the multilevel models was based on the scaled deviance, with lower deviance indicating better fit [41 ]. The data management and statistical analysis were conducted in SAS System (version 9.3, SAS Institute Inc., Cary, North Carolina).
4
Smoking Cessation Outcomes Analysis
5
Statistical Analysis of OSAS and Dental Disorders
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A descriptive statistical analysis of the data with frequency and percentage for the qualitative variables, and mean, median, standard deviation, minimum and maximum values for the quantitative variables were performed on 01/22/2018.
Significant variables at 95% were analyzed when related to high risk to OSAS and mouth breathing. To verify the association of OSA-18 and dental disorders a Chi-square or Fisher’s exact test was performed when necessary. As for age, a Test of Normality of the data was performed and as they had a symmetrical distribution, ANOVA followed by Tukey was used for the multiple comparisons. P<0.05 was considered as level of significance. The program used to perform the analysis was the SAS system, version 9.3.
Significant variables at 95% were analyzed when related to high risk to OSAS and mouth breathing. To verify the association of OSA-18 and dental disorders a Chi-square or Fisher’s exact test was performed when necessary. As for age, a Test of Normality of the data was performed and as they had a symmetrical distribution, ANOVA followed by Tukey was used for the multiple comparisons. P<0.05 was considered as level of significance. The program used to perform the analysis was the SAS system, version 9.3.
6
Statistical Analysis using SAS
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For database management and statistical analysis, we used the SAS system, version 9.3 (SAS Institute Inc., Cary, NC). Significance was a two-tailed α-level of 0.05 or less. Means and proportions were compared using the large-sample z-test or ANOVA and Fisher's exact test, respectively.
7
Cognitive Impairment and COPD in Older Chinese Adults
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Participants aged 60 years and above were included in the analysis. Estimates of prevalence of cognitive impairment were calculated separately in men and women overall and for subgroups stratified by age, education, marital status, region, urban/rural, and chronic respiratory conditions. All calculations were weighted to represent the overall national population. Weight coefficients were derived from 2010 China Census and the sampling scheme of the survey, incorporating the sampling weight, nonresponse weight, and poststratification weight.15 Logistic regression (survey logistic procedure for stratified cluster sampling) was applied to examine the relationship between COPD (self-reported COPD, chronic cough, and chronic phlegm) and cognitive impairment, from which odds ratio (OR) and 95% confidence interval (95% CI) were computed. The model was adjusted by forcing potential confounders (age, sex, geographic region, urban/rural, marital status, education, smoking status, alcohol drinking, and indoor air pollution) into the model. Interaction terms of variables (sex, urbanicity, region, education, smoking status, alcohol drinking, and indoor air pollution) and COPD were fitted in the model to test for any effect modifications. Statistical analyses were performed with SAS system, version 9.3 (SAS Institute Inc., Cary, NC, USA).
8
Evaluating Non-inferiority of Anesthetic Blocks
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The proportion of subjects with a successful block was compared between treatment groups in a binomial regression model, with both factors “group” and “centre” as fixed effects in the per protocol set. Non-inferiority of the Test in comparison with the Reference treatment was demonstrated if the lower limit of the 95% two-sided confidence interval (CI) of the difference between treatments was greater than the non-inferiority margin δ = − 0.1. Reasons for excluding subjects from the per protocol set were fully reviewed and documented during the blind review meeting before breaking study blinding.
The analysis was performed using the standard setting of SAS® PROC GENMOD.
Times to onset of sensory block and motor block, times to regression of sensory and motor block, time to administration of rescue anaesthesia or rescue analgesia, and first post-operative analgesia and time to eligibility for home discharge were analysed using Kaplan–Meier curves and compared between treatment groups by log-rank test. Safety variables were analysed descriptively.
SAS® system version 9.3 (SAS Institute Inc., Cary, NC, USA) was used for all calculations.
A p-value < 0.05 was considered statistically significant.
The analysis was performed using the standard setting of SAS® PROC GENMOD.
Times to onset of sensory block and motor block, times to regression of sensory and motor block, time to administration of rescue anaesthesia or rescue analgesia, and first post-operative analgesia and time to eligibility for home discharge were analysed using Kaplan–Meier curves and compared between treatment groups by log-rank test. Safety variables were analysed descriptively.
SAS® system version 9.3 (SAS Institute Inc., Cary, NC, USA) was used for all calculations.
A p-value < 0.05 was considered statistically significant.
9
Burn Injuries from Staten Island Superstorm Sandy
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This was a retrospective chart review of patients who sustained burn related injuries from SSHS. All patients were managed at Staten Island University Hospital Burn Center, between October 29, 2012 and November 29, 2012. Institutional Review Board approval was obtained. Data variables acquired included age, gender, race, past medical history, burn type, total body surface area of burn, hospital length of stay, need for debridement, and mortality. We compared the burn related injuries from SSHS to those reported in the 2003-2013 National Burn Repository (NBR) of the American Burn Association. Frequency distribution or descriptive statistics for demographic and baseline disease characteristics were presented for all patients. Categorical data were summarized using frequency counts, percentages and Clopper-Pearson 95 % confidence interval for proportion. Continuous outcome data were summarized by descriptive statistics such as mean, standard deviation and 95 % confidence interval. We compared the burn related injuries from SSHS to the NBR using two-sided 95 % confidence intervals for a single proportion or for a single mean. All statistical tests were 2-sided and p ≤ 0.05 was statistically significant. Data analyses were performed using the SAS® System Version 9.3 (SAS Institute Inc., Cary, NC).
10
National Estimates of Diabetes Prevalence
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All calculations were weighted to represent the overall Chinese adult population aged 18 years or older. Weight coefficients were derived from China population census data in 2010 and the sampling scheme of the present survey to obtain national estimates. Each one of the 162 study sites was categorized into underdeveloped, intermediately developed or developed region according to their gross domestic product per capita in 2009.
Demographic and metabolic features were described in overall population and in diabetes categories, using percentages (95% confidence intervals [CIs]) for categorical variables and means (95% CIs) for continuous variables. Weighted percentages (95% CIs) for prevalence, awareness, treatment and control of hypertension and dyslipidemia were estimated in overall population and in different diabetes categories.
Data were analyzed using the SAS system, version 9.3 (SAS Institute Inc, Cary, NC) and SUDAAN software, version 10.0 (Research Triangle Institute, Research Triangle Park, NC). All statistical analyses were 2-sided, and a P-value less than 0.05 was considered statistically significant.
Demographic and metabolic features were described in overall population and in diabetes categories, using percentages (95% confidence intervals [CIs]) for categorical variables and means (95% CIs) for continuous variables. Weighted percentages (95% CIs) for prevalence, awareness, treatment and control of hypertension and dyslipidemia were estimated in overall population and in different diabetes categories.
Data were analyzed using the SAS system, version 9.3 (SAS Institute Inc, Cary, NC) and SUDAAN software, version 10.0 (Research Triangle Institute, Research Triangle Park, NC). All statistical analyses were 2-sided, and a P-value less than 0.05 was considered statistically significant.
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