The prevalence of children having ever been breastfed and of children having been exclusively breastfed in the first six months after birth were calculated. Weights of complex sampling were considered in data analysis. We ran multivariable logistic regression to examine the associations of urban/rural setting, gender, age and household income per capita with the use of breastfeeding. Age was included as an independent variable to examine change in breastfeeding prevalence as age increased; such an analysis can be used to approximately assess whether the breastfeeding prevalence changes over time considering that usually breastfeeding ends in the first two years after the birth for almost all children. p < 0.05 was considered to be statistically significant. Univariable and multivariable logistic regression analyses were used to examine the associations of breastfeeding with independent variables before and after controlling for other covariates. All statistical analyses were performed using the PROC SURVEYFREQ procedure and PROC SURVEYLOGISTIC procedure of SAS 9.1 statistical software (SAS Institute Inc., Cary, NC, USA).
Sas 9.1 statistical software
Manufactured by SAS Institute
Sourced in United States
SAS 9.1 is a comprehensive statistical software package that provides advanced analytics capabilities. It offers a wide range of statistical procedures, data management tools, and reporting features to support data analysis and decision-making. The software is designed to handle large and complex data sets, enabling users to explore, analyze, and model data effectively.
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Lab products found in correlation
23 protocols using sas 9.1 statistical software
1
Breastfeeding Prevalence and Associations
2
Genetic Variants and Prostate Cancer Risk
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Deviation from Hardy-Weinberg Equilibrium (HWE) among controls for each SNP analysis was assessed by a goodness-of-fit χ2 test. Distributions of the alleles, genotypes and the categorical variables of interest between cases and controls were evaluated by Pearson's χ2 test. Traditional genetic models for association studies were conducted, including dominant, recessive, and additive models. To confirm individual effect of each SNP on PCa risk, crude ORs and 95% confidence intervals (CIs) were calculated using unconditional logistic regression models with adjustment for age, smoking status and BMI. Further stratified analyses were conducted by univariate and multivariate unconditional logistic regression methods on the best-fitting genetic models, evaluated by likelihood-ratio based estimates, to assess the associations. Finally, the homogeneity Q-test was used to identify any difference between the strata.
All statistical analyses were performed with SAS 9.1 statistical software (Cary, NC, USA). All tests were two-sided, and a P value of <0.05 was considered statistically significant.
All statistical analyses were performed with SAS 9.1 statistical software (Cary, NC, USA). All tests were two-sided, and a P value of <0.05 was considered statistically significant.
3
Ae. aegypti Larval Habitats Assessment
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Positive containers were those with one or more Ae. aegypti larvae or pupae. Proportion of wet containers that were positive in each site was determined and a Chi- square test was used to compare the distribution of positive containers between indoor and outdoor locations. Key larval habitats were defined as containers that are most productive for Ae aegypti pupae. Kruskal-Wallis test was used to compare the distribution of Ae. aegypti infestation in the four study sites, and productivity between seasons. Mosquito indices were calculated as follows: container index (the percentage of containers infested with Ae. aegypti immatures) and pupae per person index (the number of pupae over the total number of persons in a household). Data analysis was performed using SAS 9.1 statistical software (SAS Institute, Gary, NC).
4
Comparative Serum and Tissue Analysis
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The serum and tissue tests for each mouse were performed three times in parallel, and then the average was taken. SAS 9.1 statistical software (SAS Institute Inc., Cary, NC, USA) was used to analyze the data. The one-way ANOVA method according to Duncan’s multiple-range test was used to analyze whether there were significant differences between any two groups of data at the level of p < 0.05 [18 (link)].
5
Serum and Tissue Analysis of Mice
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Three parallel experiments were carried out on the serum and tissue indexes of each mouse, and then the average values were taken. Then the data were analyzed by SAS 9.1 statistical software (SAS Institute Inc., Cary, NC, USA). The one-way ANOVA method and Duncan's test were used to analyze whether there were significant differences among the groups at the level of P < 0.05.
6
Genetic Interaction Analysis for Disease Risk
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We performed the Pearson’s χ2-test for the differences in selected variables between cases and controls. Crude and adjusted ORs and their 95% CIs were computed from both univariate and multivariate unconditional logistic regression models. We further evaluated the stratified associations based on the significant genetic models accompanied by the homogeneity Q-tests for the strata. For all the significant findings, we calculated FPRP with the assumption of different prior probabilities to detect any possible false positive associations. Only FPRP values<0.2 were considered noteworthy [37 (link)]. All statistical analyses were performed with SAS 9.1 statistical software (SAS, Cary, NC, USA) unless stated otherwise. All P values were two-sided with a significance level of P<0.05.
The multifactor dimensionality reduction (MDR) analysis was conducted by the MDR V2.0 beta 8.2 software (http://www.multifactordimensionalityreduction.org/ ) to identify the best n-factor interaction model [38 (link)]. We further performed the interaction dendrograms and graphs for risk loci of this study, and the color of branches and lines referred to the type of interaction, with green-to-yellow-to-red indicating weak-to-strong interactions.
The multifactor dimensionality reduction (MDR) analysis was conducted by the MDR V2.0 beta 8.2 software (
7
Cardiovascular Risk Factors Assessment
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Continuous variables are presented as mean ± SD, and categorical variables presented as absolute and relative frequencies (%). The risk factor for atherosclerosis of IT, MT, CIMT, and the IT/MT ratio was assessed with univariate analysis and after adjustment for age and sex was analyzed. Multivariate analysis was performed to determine the independent risk factors of MT, mean and maximal CIMT, and the IT/MT ratio. All reported p-values were two-sided, and p-values of less than 0.05 were considered to indicate statistical significance. Intraclass correlation coefficient (ICC) is a statistical method for measurement of intra- and interobserver reliability. Reliability was assessed by replicating measurements for 50 patients. ICC assesses the consistency of multiple measurements of the same quantity. In general, the ICC ranges from 0.00 (no agreement) to 1.00 (perfect agreement). Also, an ICC value of 0.7–0.8 indicates strong agreement, and an ICC value > 0.8 indicates excellent agreement. ICC measures the proportion of the variance that is attributed to different observers. A 95% confidence interval was estimated for each ICC to estimate the precision and the range of the correlation. All statistical analyses were conducted in SAS 9.1 statistical software (SAS Institute Inc., Cary, NC, USA).
8
Survival Analysis of Neurological Outcomes
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Survival curves were plotted using the Kaplan-Meier method, and the significance of the differences between groups was determined using a log-rank test that considered the effects of age. The median survival time and 95 % confidence interval (CI) were then estimated based on the Brookmeyer and Crowley method [5 ]. A p value <0.05 was considered statistically significant. Chi-squared test statistics and p values were calculated based on the log-rank test of specific pairs. For variables with 2 subgroups, a p < 0.05 was considered statistically significant. For variables with 3 subgroups, a p < 0.0167 was considered statistically significant (the Bonferroni correction method was used to suppress a spurious significant difference).
Univariate and multivariate Cox proportional hazard regressions were used to detect possible prognostic factors. To investigate the most significant factors, factors significantly impacted with survival in univariate analysis were included in multivariate analysis. Pre-op palsy score was considered to be the important factor, so it was included in multivariate analysis even no significance in univariate analysis. A p < 0.05 was considered statistically significant. All analyses were performed using SAS 9.1 statistical software (SAS Institute, Inc, Cary, NC, USA).
Univariate and multivariate Cox proportional hazard regressions were used to detect possible prognostic factors. To investigate the most significant factors, factors significantly impacted with survival in univariate analysis were included in multivariate analysis. Pre-op palsy score was considered to be the important factor, so it was included in multivariate analysis even no significance in univariate analysis. A p < 0.05 was considered statistically significant. All analyses were performed using SAS 9.1 statistical software (SAS Institute, Inc, Cary, NC, USA).
9
Antifungal Activity Assessment Protocol
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For antifungal activity, a two-way analysis of variance (ANOVA) was performed, and the separation of means was obtained with the procedures of SAS 9.1 statistical software (SAS Institute, Cary, NC, USA), using Duncan’s multiple range test with a significance level of p < 0.05.
10
Multivariate Analysis of ASCVD Risk Factors
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Continuous variables are presented as mean ± standard deviation (SD), and categorical variables are presented as absolute and relative frequencies (%). A t-test was used to compare the means when there were two groups. Proportions were compared using the two-way tables and chi-square tests. Multivariate analyses using the Cox proportional hazard regression model were applied to the variables that were significant in the univariate analysis and known important risk factors for ASCVD. In addition, multivariate analyses were schematized using a restricted cubic spline curve. Two-sided p-values of ≤ 0.05 indicated statistical significance. Statistical analyses were conducted using SAS 9.1 statistical software (SAS Institute, Cary, NC, United States) and R version 3.6.3 (The R Foundation for Statistical Computing, Vienna, Austria1 ).
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