Patient characteristics were summarized as mean values and standard deviation for continuous parameters, or absolute count and percentage for categorical parameters. The student t-test was used to compare continuous parameters, while χ2-test was used for categorical parameters. Demographic data and clinical manifestations in patients with stroke history were compared with first-ever stroke patients. The number and proportion of missing data for these variables were shown as following: BMI (1220, 9.8%), family history of stroke (805, 6.5%), NIHSS at admission (505, 4.1%), and Glasgow Coma Scale (GCS) (24, 0.2%). The associations between stroke history and death, further recurrence, and mRS 3-6 were analyzed using multivariable logistic regression after adjusting for potential confounders including age, gender, history of disease,smoking, drinking, NIHSS, medicine use before admission and during hospitalization, and complications during hospitalization. All data were analyzed by SAS version 9.4 statistical software (SAS Institute Inc, Cary, NC).
Sas version 9.4 statistical
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SAS version 9.4 is a software package that provides advanced statistical analysis and data management capabilities. It offers a range of tools for data manipulation, modeling, and reporting. The core function of SAS version 9.4 is to enable users to analyze data, generate reports, and make informed decisions based on statistical insights.
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Lab products found in correlation
4 protocols using sas version 9.4 statistical
1
Associations of Stroke History
2
Concordance Between Plasma and Tissue RAS Testing
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Categorical variables were summarised in numbers and percentages, continuous variables were presented as medians, minima and maxima. Concordance between plasma and tissue RAS testing was determined using a Kappa statistic (kappa) with 95% confidence interval (CI). Positive percent agreement (PPA), negative percent agreement (NPA) and overall percent agreement (OPA) were also calculated. For MAF levels correlations with clinical variables, we performed non-parametric statistics (Mann–Whitney U test for dichotomous and Kruskal–Wallis test for polychotomous variables). All statistical tests were considered significant when P < 0.05. Statistical analyses were performed using the SAS version 9.4 statistical software.
3
Impact of Child Tax Credit on Low Birth Weight
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We used a comparison-population, interrupted time series design (an ecologic study design) to test the hypothesis that the log odds of LBW among monthly cohorts born to parous people after the CTC payment (ie, July-December 2021) would differ from their expected values. We derived expected values from preterm births among parous people during CTC payments, LBW among births to nulliparous people (the comparison group) during CTC payments, and autocorrelation in LBW among births to parous people prior to CTC payments. We further adjusted for shocks to (ie, outliers in) LBW that occurred prior to the CTC payments, such as those that could have arisen from the COVID-19 pandemic. We implemented this design using the following steps (further detail is provided in the eMethods in Supplement 1 ):
The data management was performed using SAS, version 9.4 statistical software (SAS Institute Inc), and the time series analyses were performed using SCA Statistical System, version 5.2 (Scientific Computing Associates). A 2-sided P < .05 was considered significant.
The data management was performed using SAS, version 9.4 statistical software (SAS Institute Inc), and the time series analyses were performed using SCA Statistical System, version 5.2 (Scientific Computing Associates). A 2-sided P < .05 was considered significant.
4
Survival Analysis of Mortality Factors
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The nonparsimonious approach using variables such as age, race/ ethnicity, etc. was used to construct models. Descriptive statistics for the different variable were reported. Univariable analysis of each variable was performed using chi-square test for categorical data and ANOVA for numerical data. The Kaplan-Meier method was used for survival analysis. Univariable Cox proportional hazard regression was used to identify factors significantly associated with the risk of deaths for all causes. Factors that were statistically significant in univariable analysis were included in the multivariable model. The multivariable Cox proportional hazards regression analysis was used to determine independent significant factors associated with the risk of death for all causes, and the hazard ratios (HR) and confidence intervals (CI) were calculated. A p-value 0.05 was considered statistically significant. Multicollinearity was detected using Variance Inflation Factors (VIF). VIF greater than 4 indicates multicollinearity. All statistical analyses were performed using SAS Version 9.4 statistical software, (SAS Institute Inc., Cary, NC, U.S.A., 2013).
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