Statistical analyses were performed using the STATA 13.1, StataCorp. 2016 Stata Statistical Software: Release 13.1 College Station, TX: StataCorp LP. The results were tested for the normality of their distribution using the Shapiro–Wilk test and for the equality of variances using Levene’s test. In order to assess the statistical significance of differences in RVA prevalence between sampling seasons and age groups, the simple logistic regression and the nonparametric Spearman rank correlation test were used, respectively. Decisions on statistical relevance were made at the significance level of p ≤ 0.05.
Stata statistical software release 13
Manufactured by StataCorp
Sourced in United States
Stata Statistical Software: Release 13 is a comprehensive software package designed for data analysis, statistical modeling, and graphics. It provides a wide range of statistical tools and functions to support researchers, analysts, and professionals in various fields.
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Lab products found in correlation
Stata 13, by StataCorp (38 mentions)
Stata, by StataCorp (31 mentions)
Stata version 13, by StataCorp (29 mentions)
Sas version 9, by SAS Institute (12 mentions)
Stata software, by StataCorp (10 mentions)
Stata v 13, by StataCorp (4 mentions)
Sas 9, by SAS Institute (4 mentions)
Stata software version 13, by StataCorp (4 mentions)
Stata se 13, by StataCorp (2 mentions)
Stata se version 13, by StataCorp (2 mentions)
Spss statistics for windows version 22, by IBM (2 mentions)
Stata software 13, by StataCorp (1 mentions)
Stata se 13.1 for mac, by StataCorp (1 mentions)
Spss version 22, by IBM (1 mentions)
Spss statistics for windows version 23, by IBM (1 mentions)
Spss statistics 22, by IBM (1 mentions)
Spss for windows version 16, by IBM (1 mentions)
Sas stat software, by SAS Institute (1 mentions)
Version 18, by MedCalc (1 mentions)
Stata 13.0 se, by StataCorp (1 mentions)
252 protocols using stata statistical software release 13
1
Seasonal and Age-Dependent RVA Prevalence
2
Survival and Pseudoprogression in Glioma
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The overall survival (OS) and median progression-free survival (PFS) were analyzed for 18 patients. OS was defined as the time from the start of treatment to patient death or last follow-up and PFS was defined as the treatment initiation time to first disease progression time or last follow-up time.
Local advancement was determined depend on MRI with contrast enhancement according to Response Evaluation Criteria (RECIST Version 1.1). To confirm pseudoprogression (PsP) occurred after treatment, the serial MRIs was utilized and analyzed. PsP defined as the transient worsening of enhancing abnormalities or mass effect on MRI after radiotherapy. Treatment-related toxicities were evaluated according to Common Terminology Criteria for Adverse Events version 4.03 (CTCAE v4.03). Any new symptoms or increasing neurological symptoms happened after radiotherapy without radiographic disease progression by MRI was deemed radiation-related.
The Kaplan–Meier method was used to calculate the survival, and the results were compared with those of the log-rank test. In multivariate analysis, risk factors for OS from treatment were analyzed by Cox regression model. P ≤ 0.05 was considered statistical significance. All statistical analyses were computed by using Stata version 13.1 (StataCorp., 2013. Stata Statistical Software: Release 13.1 College Station, TX, USA: StataCorp LP).
Local advancement was determined depend on MRI with contrast enhancement according to Response Evaluation Criteria (RECIST Version 1.1). To confirm pseudoprogression (PsP) occurred after treatment, the serial MRIs was utilized and analyzed. PsP defined as the transient worsening of enhancing abnormalities or mass effect on MRI after radiotherapy. Treatment-related toxicities were evaluated according to Common Terminology Criteria for Adverse Events version 4.03 (CTCAE v4.03). Any new symptoms or increasing neurological symptoms happened after radiotherapy without radiographic disease progression by MRI was deemed radiation-related.
The Kaplan–Meier method was used to calculate the survival, and the results were compared with those of the log-rank test. In multivariate analysis, risk factors for OS from treatment were analyzed by Cox regression model. P ≤ 0.05 was considered statistical significance. All statistical analyses were computed by using Stata version 13.1 (StataCorp., 2013. Stata Statistical Software: Release 13.1 College Station, TX, USA: StataCorp LP).
3
Multivariate Analysis of Cell Characteristics
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For comparison of data, normality was checked using histogram and Q-Q plots and statistical analyses were performed using two-ANOVA on cell viability, cell proliferation, gene expression containing (microstructure*time) and relative number of cells (substrate*chemistry) information.
Multivariate ANOVA was performed on hypoxia data containing (microstructure*oxygentension*time). All data that passed ANOVA was either analyzed with pairwise comparisons of means using tukey post-hoc test or one-way ANOVA. Student's t-test was performed on KI67 positive cells. Data are presented as mean ± SD of three independent experiments in triplicates (n=3). Stata Statistical Software: release 13.1 (StataCorp LP) was used. P-values <0.05 were considered significant.
Multivariate ANOVA was performed on hypoxia data containing (microstructure*oxygentension*time). All data that passed ANOVA was either analyzed with pairwise comparisons of means using tukey post-hoc test or one-way ANOVA. Student's t-test was performed on KI67 positive cells. Data are presented as mean ± SD of three independent experiments in triplicates (n=3). Stata Statistical Software: release 13.1 (StataCorp LP) was used. P-values <0.05 were considered significant.
4
Factors Influencing Alcohol Guideline Awareness
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Data were analysed using Stata® Statistical Software: Release 13.1 (StataCorp, College Station, TX, USA). Sample weights were applied to gender, region and age to adjust for the under sampling of males and older people relative to the UK alcohol drinking population. Results are displayed using weighted data are presented, unless specified. Univariate binary logistic regression was used to explore factors associated with guideline awareness and guideline knowledge. Univariate ordinal logistic regression was used to explore factors associated with governmental responsibility and intended behaviour change. Significant factors were then were then entered into a multivariable logistic regression, with step-wise elimination of non-significant variables to determine independent predictors. The association of guideline awareness and message recognition was analysed using chi squared tests.
5
Comprehensive Statistical Analysis of μCT and Proteomic Data
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The statistical analysis was performed using Stata Statistical Software: release 13.1 (StataCorp LP). Parameters obtained from μCT data were not normally distributed and were statistically evaluated using Wilcoxon rank sum test for equal means on total bone volume between the groups at each time point. Data are presented as median ± SEM. ELISA s-RANKL data were analyzed using the Student’s t-test. For comparison of proteomic data, normality was checked using Shapiro–Wilks methods, histogram and QQ plots. Statistical analysis on normally distributed Olink Proseek data was performed using one-way ANOVA, and data not following normal distribution were compared using the Kruskal–Wallis rank test. Data are presented as mean ± SD; p-values < 0.05 were considered statistically significant.
6
Evaluating Impact of Health Education
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All statistical analyses were performed using Stata statistical software release 13.1 (StataCorp, College Station, TX, USA). All data were summarized as n of patients (%). Frequencies before and after education were compared using χ2-test or Fisher’s exact test as appropriate. Questions about number of emergency department (ED) visits, hospitalizations, and school missing times were compared using Wilcoxon matched-pairs signed-ranks test. A P-value < 0.05 was considered statistically significant.
7
Immunological Impact Analysis in Microdialysates
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An explorative cut-off was created for the analysis of immunological impact quantified in microdialysates and plasma specimens. Plasma proteins with a data measured frequency below <80% were excluded from statistical tests and only used descriptive. Proteins with higher measured frequency in microdialysates in comparison to plasma were included. In order to identify possible outliers, and to evaluate consistency of the data, principal component analyses (PCAs) were performed and visualized. PCA was performed for all samples grouped by different variables of interest in R version 3.6.1. PCA plots were illustrated as scatterplots with percentage of variability. All NPX data were checked for normality using histogram and QQ plots. All data are presented as mean ± SE and differences between groups where considered significant when p < .05*. One-way ANOVA was performed on arbitrary log-2 scaled NPX values for microdialysates and plasma comparison, and for healthy bone and infected bone comparison. The statistical analyses were performed using STATA statistical Software: release 13.1 (StataCorp LP, Collage Station, TX, USA).
8
Statistical Analysis of Survey Costs
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Descriptive analyses including frequencies, mean, median, s.d., minimum and maximum values were conducted for all the questions in the survey and for the direct, indirect and total costs. Statistical analyses were performed using STATA Statistical Software: Release 13.1 College Station, TX, USA.
9
Weighted Analysis of NSHAP Vaginal Swab Data
10
Mediating Role of %ΔFVC in BMI-BHR Link
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A directed acyclic graph determined which covariates should be included in regression models for both association and mediation analyses. A role for %ΔFVC as a mediator between BMI and BHR was examined following the steps recommended by Kenny using the "bivariate mediation" module of Stata software [25] . Using multiple logistic regression models, relationships between BMI and BHR were explored while adjusting for sex, baseline lung function, atopy, asthma, smoking, family history of asthma, SES, and ICS use, with the results presented as an odds ratio with a 95% confidence interval. Prevalence estimates and regression models were reweighted using the known sampling fractions derived from the 1968, 1974 and 2004 surveys [26] . Interactions between BMI and sex and between BMI and asthma were estimated and examined further if the interaction p-value was ⩽0.10. All analyses were performed using Stata Statistical Software: Release 13.1 (StataCorp, College Station, TX, USA).
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