Preliminary analysis of the dependent variable of interest, i.e., number of positive language sites, revealed a distribution of count data with a Poisson distribution that was zero inflated and over dispersed, with the variance (9.5) greater than the mean (2.9). Therefore, assessing the contribution of age to the number of naming sites required a negative binomial regression analysis. Criteria for goodness of fit were met (i.e., deviance 1.1, Deviance over degrees freedom, >1). Given the variability in “exposure,” (i.e., exposure to stimulation), number of sites tested was included as the offset term to control for this potential confound. Negative binomial regression models were used to assess the potential influence of age on the number of ESM-identified naming sites, controlling for FSIQ and epilepsy onset age. Paired t-test was use to compare number of ESM-identified AN and VN sites per patient, and Pearson correlations were used to assess relations among predictor variables considered for regression analyses. Exploratory analyses included Pearson correlations between number of language sites and cognitive performance, and multivariate followed by univariate ANOVA comparing the youngest and oldest thirds of the study sample with regard to demographic, clinical, mapping and performance variables. All analyses were conducted using SAS 9.4 and SPSS 24.
Sas 9
Manufactured by IBM
Sourced in United States
SAS 9.4 is a software suite that provides statistical analysis, data management, and business intelligence capabilities. It is designed to help organizations effectively manage, analyze, and report on their data.
Automatically generated - may contain errors
Lab products found in correlation
Spss 15, by StataCorp (20 mentions)
Stata 10, by MedCalc (19 mentions)
Sas 9, by SAS Institute (7 mentions)
Spss 19, by IBM (5 mentions)
Spss statistics 25, by IBM (4 mentions)
Prism 5, by GraphPad (3 mentions)
Nquery advisor 7, by SAS Institute (3 mentions)
Stata 10, by IBM (2 mentions)
Systat 12, by IBM (2 mentions)
Spss 20, by IBM (2 mentions)
Spss statistics 24, by IBM (2 mentions)
Spss statistics version 25, by IBM (2 mentions)
Spss 24, by IBM (2 mentions)
Spss 25, by IBM (2 mentions)
Prism 7, by GraphPad (2 mentions)
Spss v24, by IBM (1 mentions)
Spss v22, by IBM (1 mentions)
Spss version 22, by IBM (1 mentions)
Spss 26, by StataCorp (1 mentions)
Spss v26, by IBM (1 mentions)
137 protocols using sas 9
1
Assessing Epilepsy-Related Language Mapping
2
Exploratory Phase 2 Trial of Abiraterone
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Patient disposition and efficacy analyses were performed on data from the intention-to-treat (ITT) population (equivalent to Full Analysis Set [FAS]). All patients who received any part of abiraterone acetate were included in the analysis of safety (safety population). Evaluation of an eligible and/or per-protocol population was not specified in the protocol. The baseline measurement was the last value on or before the date of first study treatment. All statistical analyses were performed using SAS 9.4 and SPSS 24.0.
This was an exploratory phase 2 trial where neither the primary endpoint radiographic progression-free survival (rPFS) nor the secondary endpoints were powered for statistical significance (all results are to be interpreted in the exploratory sense). Each treatment arm should include 30 patients evaluable for the primary endpoint. Assuming a drop-out rate of 15% in each arm it was estimated that 70 patients needed to be recruited for this trial.
This was an exploratory phase 2 trial where neither the primary endpoint radiographic progression-free survival (rPFS) nor the secondary endpoints were powered for statistical significance (all results are to be interpreted in the exploratory sense). Each treatment arm should include 30 patients evaluable for the primary endpoint. Assuming a drop-out rate of 15% in each arm it was estimated that 70 patients needed to be recruited for this trial.
3
Factors Influencing COVID-19 Vaccine Attitudes
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We used statistical software, SAS 9.4 and SPSS 27.25
,26 The study population was primarily categorised into the following two groups: ‘vaccine compliant’ and ‘vaccine hesitant’. We computed a new variable ‘perceived risk’ based on the following parameters: (a) dichotomous variables (yes vs no): age >60 years, HCW, family member diagnosed with COVID-19; and (b) stress (in 1–5 Likert scale) related to potential infection, the risk of severe disease, lack of hospital facility and COVID-19 prevalence in the US state of residence. Cronbach's alpha was used to test internal consistency among the variables contributing to the ‘perceived risk’. The perceived risk scores were consolidated by ‘factor reduction’ into a nominal variable (range: 0–10). We also included questions to understand the reasons for refusal of the COVID-19 vaccine, such as concern with potential adverse effects or doubt about its efficacy and necessity. Finally, we enquired about the preferred source of information such as television, social media, Centers for Disease Control and Prevention (CDC), state health websites and personal communication.
,26 The study population was primarily categorised into the following two groups: ‘vaccine compliant’ and ‘vaccine hesitant’. We computed a new variable ‘perceived risk’ based on the following parameters: (a) dichotomous variables (yes vs no): age >60 years, HCW, family member diagnosed with COVID-19; and (b) stress (in 1–5 Likert scale) related to potential infection, the risk of severe disease, lack of hospital facility and COVID-19 prevalence in the US state of residence. Cronbach's alpha was used to test internal consistency among the variables contributing to the ‘perceived risk’. The perceived risk scores were consolidated by ‘factor reduction’ into a nominal variable (range: 0–10). We also included questions to understand the reasons for refusal of the COVID-19 vaccine, such as concern with potential adverse effects or doubt about its efficacy and necessity. Finally, we enquired about the preferred source of information such as television, social media, Centers for Disease Control and Prevention (CDC), state health websites and personal communication.
4
Analyzing AMD Risk Factors
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Chi-square tests were used to analyze categorical variables, including demographics and comorbidities, while continuous variables were assessed with two-sample t-tests. Kaplan–Meier curves were used to depict cumulative AMD incidence and Cox regression was used to estimate AMD risk, adjusted for age, sex, and comorbidities and stratified by age and sex to determine DR and AMD association. Data were analyzed via SAS 9.4 and SPSS 22.0, with p < 0.05 set as the significance level.
5
Postpartum Depression Risk Factors
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The normality of the data distribution was examined by the Kolmogorov–Smirnov normality test. The characteristics of the pregnant women and newborns were described using median and interquartile range (IQR) for continuous variables and frequencies or proportions for categorical variables. Participants were divided into two groups depending on whether they developed postpartum depressive symptoms (EPDS < 10 vs. EPDS ≥ 10). Differences in maternal characteristics, trimester-specific sleep quality, psychological health, exercise status, pregnancy outcomes and newborn outcomes between the two groups were compared using Mann Whitney U test, Wilcoxon tests, chi-square tests or Fisher’s exact tests when applicable. Variables with a statistical significance of lower than 0.1 were included in further multivariate logistic regression models. The effects of trimester-specific sleep quality and mental status on the postpartum depressive symptoms were figured out using mixed-effects models. All the statistical analyses above were performed with SAS 9.4 and SPSS 25.0. The mediation model was constructed by SPSS 25.0 and AMOS 26.0; the mediation effect was conducted by the bootstrap method. An α-level of 0.05 was used for each analysis.
6
Evaluating DACQ Awareness Factors
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The levels of awareness, as well as the distribution of the DACQ total and domain scores with respect to the demographic and clinical characteristics, were evaluated at study entry.
Two-sample t-test and ANOVA (or respective analogous nonparametric tests, if appropriate) were used to evaluate the statistical significance of differences in scores among different groups of patients. Moreover, the associations between awareness and patient age, gender, COPD severity (based on the GOLD 2017 risk categories),5 and impact of symptoms (by means of the CAT score) were investigated with a multivariable linear regression analysis.
Analyses were performed using SAS 9.4, SPSS v. 20.0, and WINSTEPS 3.72.3. Statistical analyses, project management, and quality control were performed by MediNeos Observational Research (Modena, Italy).
Two-sample t-test and ANOVA (or respective analogous nonparametric tests, if appropriate) were used to evaluate the statistical significance of differences in scores among different groups of patients. Moreover, the associations between awareness and patient age, gender, COPD severity (based on the GOLD 2017 risk categories),5 and impact of symptoms (by means of the CAT score) were investigated with a multivariable linear regression analysis.
Analyses were performed using SAS 9.4, SPSS v. 20.0, and WINSTEPS 3.72.3. Statistical analyses, project management, and quality control were performed by MediNeos Observational Research (Modena, Italy).
7
Evaluating Healthcare Workers' Knowledge
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Descriptive and inferential statistics were performed with SAS 9.4 & SPSS 25. We examined the distribution of participants according to relevant demographic characteristics and ethnicity. Frequencies were compared with the use of the χ2 test, while continuous variables were compared with the use of Student’s t-test. In all analyses, statistical significance was determined by P < 0.05.
Responses to single statements at baseline were compared between groups using percentages of correct responses on each statement. χ2 tests were used for each statement to verify differences in correct responses between native HCWs and foreign HCWs.
Responses to single statements at baseline were compared between groups using percentages of correct responses on each statement. χ2 tests were used for each statement to verify differences in correct responses between native HCWs and foreign HCWs.
8
Evaluating Questionnaire Dimensionality and Effectiveness
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The data were analyzed using SAS (9.4) and SPSS 27 [17 ]). First, descriptive statistics were calculated to examine the distributions of all variables. Second, Cronbach’s alpha was used to measure scale reliability. Third, because Cronbach’s alpha can be low when there are a small number of items, following Bartlett’s test of Sphericity for the strength of correlations, confirmatory factor analysis was conducted to examine dimensionality using principal component analysis to determine correlations between the questionnaire items and constructs.
Fourth, a univariable and multivariable linear mixed model [18 ] was used to test for a statistically significant change in average mean scores between pre and posttest after adjusting for demographic covariates (age, sex, education, and number of children). Missing cases were removed from the pretest, posttest, and all demographic variables. Model fit and assumptions were assessed via a visual assessment of residuals. Statistical significance was determined based on 95% confidence intervals and an alpha level of less than 0.05. Multiple comparison tests were applied where appropriate.
Fourth, a univariable and multivariable linear mixed model [18 ] was used to test for a statistically significant change in average mean scores between pre and posttest after adjusting for demographic covariates (age, sex, education, and number of children). Missing cases were removed from the pretest, posttest, and all demographic variables. Model fit and assumptions were assessed via a visual assessment of residuals. Statistical significance was determined based on 95% confidence intervals and an alpha level of less than 0.05. Multiple comparison tests were applied where appropriate.
9
Predicting ALL Prognosis via Stepwise Regression
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In the study, March 28, 2018, was used as the end of the collection of the patients’ treatment outcomes. Event-free survival (EFS) was defined as time to ALL relapse, second malignant neoplasms (SMNs) or death, with censoring at last contact.
Stepwise logistic regression was used to select marker genes that can predict the prognosis of ALL patients in this study. First, several candidate models were selected according to the corrected Akaike’s information criterion (AICc) and Bayesian information criterion (BIC) values. The lower the AICc and BIC values, the better the model. Then, fivefold cross-validation was used to determine the optimal model, and the model with the lowest misclassification rate in the validation set was considered the final prediction model. Finally, the patients were classified into two groups, the good-risk (GR) group and the poor-risk (PR) group, by the prediction model. The Kaplan–Meier method and log-rank test were used to estimate and compare the survival curves of the two groups. Comparisons between the clinical characteristics, early treatment responses and prognoses of the two groups were performed using the Chi square test or Fisher’s exact test, where appropriate. A P value of less than or equal to 0.05 was considered significant. All analyses were performed using SAS 9.4 and SPSS 16.0 for Microsoft Windows software.
Stepwise logistic regression was used to select marker genes that can predict the prognosis of ALL patients in this study. First, several candidate models were selected according to the corrected Akaike’s information criterion (AICc) and Bayesian information criterion (BIC) values. The lower the AICc and BIC values, the better the model. Then, fivefold cross-validation was used to determine the optimal model, and the model with the lowest misclassification rate in the validation set was considered the final prediction model. Finally, the patients were classified into two groups, the good-risk (GR) group and the poor-risk (PR) group, by the prediction model. The Kaplan–Meier method and log-rank test were used to estimate and compare the survival curves of the two groups. Comparisons between the clinical characteristics, early treatment responses and prognoses of the two groups were performed using the Chi square test or Fisher’s exact test, where appropriate. A P value of less than or equal to 0.05 was considered significant. All analyses were performed using SAS 9.4 and SPSS 16.0 for Microsoft Windows software.
10
Urinary Cisplatin Kinetics and ADR Severity
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Statistical analysis was performed using the Statistics Program for Social Sciences for Windows (SPSS 16.0, SPSS Inc., Chicago, IL, USA) and Statistical Analysis System for Windows (SAS 9.4. SAS Institute Inc., 2002–2008, Cary, NC, USA). The significance level for all analyses was 5% (p < 0.05). Continuous and categorical data were described as mean and percentage, respectively. Wilcoxon test (paired samples) and ANOVA with repeated measures were used to analyse changes between assessments (different times). The Spearman Correlation test was used to check the linear correlation between the kinetics of urinary excretion of cisplatin and severity of ADRs and also between ADRs. For correlations that were statistically significant (p < 0.05), we considered only correlation coefficient (R) ≥ 0.5 and R ≤ −0.5. We also used univariate linear and logistic regression analyses to check the association between the kinetics of urinary cisplatin excretion and severity of ADRs.
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