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Sas statistical software for windows version 9

Manufactured by SAS Institute
Sourced in United States

SAS statistical software for Windows, Version 9.3 is a comprehensive data analysis and reporting tool. It provides a suite of applications for data management, statistical analysis, and visualization. The software is designed to work on the Windows operating system.

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14 protocols using sas statistical software for windows version 9

1

Survival Analysis of Metformin in Lung Cancer

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Cox proportional hazard models with time-dependent covariate were used to compute hazard ratios (HRs) and the accompanying 95.0% CIs after adjustment for the CCI, the aDCSI, sociodemographic characteristics (age, sex, income, and level of urbanization), diabetes drugs other than metformin, and lung cancer treatments. The outcomes of different patient groups, stratified according to sex, age, the CCI, the aDCSI, diabetes drugs other than metformin, and lung cancer treatments, were also analyzed. All statistical analyses were conducted using SAS statistical software for Windows, version 9.4 (SAS Institute Inc., Cary, NC, USA). A two-tailed p < 0.05 was considered statistically significant.
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2

Population-based survey analysis

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This paper is descriptive with some basic statistical tests. Categorical variables are presented as numbers and percentages, and continuous variables as means and standard deviations. The median number of days until the response is also presented. When testing for differences between groups, two-sample t-tests were used for comparisons of means, allowing for unequal variances, while Pearson’s chi-square tests were used when comparing categorical variables [29 ]. Comparisons of medians were done by the Kruskal–Wallis test [29 ].
As Statistics Norway did not register sex, age and municipality for the invitations that were returned unopened, response rates according to these variables were based on the original samples (n = 6176 for first-generation and n = 6433 for second-generation). The calculations were performed using aggregated data on the number of invitees and the number of respondents in each subgroup.
The SAS statistical software for Windows version 9.4 (SAS Institute Inc., Cary, NC, USA) was used for data management, calculations and statistical analyses. All tests were two-sided, and the significance level was set to 5%.
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3

Comparative Statistical Analysis of Biomarkers

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Quantitative data are expressed as means ± SD. Statistical analysis was performed by ANOVA followed by Bonferroni multiple-comparison post hoc test. Statistical analysis was performed using SAS statistical software for Windows version 9.4 (SAS institute, Cary, NC). A probability value < 0.05 was considered statistically significant.
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4

Comprehensive Statistical Analysis of Survival

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Mean and standard deviation were used to describe all continuous variables when the data were uniformly distributed. For continuous variables that were not uniformly distributed, median and interquartile range statistics were used. Analysis of variance (ANOVA) was used to compare the means of continuous uniformly distributed variables. Chi-squared tests were used to analyze the categorical variables, which are presented as numbers and percentages. Assessment of differences in overall survival between the periods studied was conducted with Kaplan–Meier statistics and log-rank tests. SAS statistical software for Windows version 9.4 was used for all statistical analyses. A p-value of <0.05 was used to define statistical significance.
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5

SAS Statistical Analysis for Windows

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SAS statistical software for Windows version 9.2 (SAS Institute Inc., Cary, NC, USA) was used for both data management and statistical analysis. The data presented in this paper are primarily of a descriptive character, with some statistical tests between categorical variables using chi-square testing.
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6

Investigating Treatment Efficacy for Chronic Condition

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The data are expressed as the mean±SD. One-way ANOVA with bonferroni correction was performed for multiple comparation. Statistical analyses were performed using the SAS statistical software for Windows version 9.2. A probability value<0.05 was considered statistically significant.
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7

Comparative Hearing Threshold Analysis

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Descriptive statistics were performed; results were reported as frequencies with percentages, means with standard deviations, or medians with ranges. Univariate parametric analyses utilized Fisher’s exact and chi-square analyses for categorical data and students T-test for continuous variables. Non-parametric tests were performed where appropriate. Paired univariable analyses were conducted to determine treatment responses. Repeated measures linear regression analysis was performed for comparative and predictive modeling of hearing thresholds to account for paired ears within patients. All tests considered p <0.05 as statistically significant. Statistical analyses were performed using SAS statistical software for Windows Version 9.3 (SAS Institute, Cary, North Carolina, USA).
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8

Comorbidities and Stroke Mortality

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The prevalence of comorbidities was calculated and the outcome was measured by in-hospital mortality. Patient characteristics were described and group differences were tested by using independent t-tests and Chi-squared tests. Cox proportional hazards regression was used to identify the risk factors associated with death in the whole sample. C-statistics was used to analyze the predictive ability of ECI and CCI score in inhospital death in the stroke and compared with tests of equality for Receiver Operating Characteristic (ROC) curves.
SAS statistical software for Windows, Version 9.3 (SAS Institute, Cary, NC, USA) and SPSS statistical software for Windows, Version 19 (Chicago, IL, USA) were used (IBM, Armonk, NY, USA). A P-value of ≤ 0.05 was considered significant.
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9

Cancer Risk in Patients with PID

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The incidences of newly diagnosed breast cancer, ovarian cancer, or uterine cancer in PID patients and controls were calculated, and independent t tests and chi-squared tests were used to examine differences in demographic characteristics between the PID patients and controls. A Cox proportional-hazards regression model was constructed to calculate the hazard ratio (HR) of breast cancer, ovarian cancer, or uterine cancer of the PID and control cohorts, respectively. Control variables, such as age, urbanization, monthly income, common co-morbidities including hypertension, diabetes mellitus, dyslipidemia, congestive heart failure, chronic pulmonary diseases, coronary artery diseases, and cerebrovascular diseases were included as covariates in the multivariate model to calculate adjusted HR. SAS statistical software for Windows, version 9.3 (SAS Institute, Cary, NC, USA), was used for data extraction, computation, data linkage, processing, and sampling. All other statistical analyses were performed using SPSS statistical software for Windows, version 20 (IBM, Armonk, NY, USA). Results for comparisons with a P value of less than .05 were considered as a statistically significant relationship.
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10

Osteoporosis Incidence in Alzheimer's Disease

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The incidence of newly diagnosed osteoporosis in the AD and control cases was the primary outcome in this study. We compared the distributions of the demographic characteristics including common comorbidities between the 2 cohorts by using independent t tests for continuous variables and a χ2 test for categorical variables. To investigate potential surveillance bias, subgroups were stratified according to the follow-up periods. Furthermore, a Cox proportional hazard regression model was used to calculate the hazard ratios (HR) of newly diagnosed osteoporosis in the AD and control cohorts.
We used the SAS statistical software for Windows, Version 9.3 (SAS Institute, Cary, NC) for all data processing and analyses. Some statistical analyses were performed using the SPSS software, Version 20 (IBM, Armonk, NY). P < 0.05 was considered to be statistically significant.
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