Data were analyzed using Stat View for Windows, version 5.0.1 software (SAS Institute Inc., Cary, NC) and presented as mean +/− standard deviation (SD) or a standard number representing the total. The Student’s t-test or Mann–Whitney U test were used to compare continuous variables (mean ± SD), while the chi-square test or Fisher’s exact test were used to calculate the clinical and ongoing pregnancy rates. A P value < 0.05 was considered to be statistically significant with bilateral testing. Then mean values of clinical outcomes were evaluated to calculate the study power by post-hoc test using G*Power software (version 3.0.1). The power calculation showed that two samples of 220 patients resulted in a power of 80 % if the difference in percentage was 15 %. Multivariate logistic regression analysis was used to test the correlation between clinical variables on one hand and the occurrence of pregnancy on the other. Odds ratios (ORs) and 95 % confidence intervals (95 % CIs) were calculated separately for each factor. Confidence intervals exclusive of unity were considered to be significant.
Statview for windows version 5
Manufactured by SAS Institute
Sourced in United States
StatView for Windows version 5.0 is a data analysis and presentation software designed for researchers and scientists. It provides a wide range of statistical analysis tools and graphical capabilities for data visualization. The software is capable of handling various data formats and enables users to perform statistical analyses, create interactive charts and graphs, and share their findings effectively.
Automatically generated - may contain errors
20 protocols using statview for windows version 5
1
Statistical Analysis of Clinical Outcomes
2
Statistical Analysis of Survival Outcomes
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Quantitative variables were presented as the mean ±SD, and with median when normality cannot be statistically established. The Anderson–Darling test was used to assess normality of variables’ distribution. The independent t-test or the Mann–Whitney test were performed for normal or non-normal variables, respectively. Qualitative variables were presented as absolute frequencies and percentages and to evaluate significant differences in data frequency we analyzed 2 × 2 contingency tables by the Fisher exact test. Tables with size larger than 2 × 2 were examined by the Chi-squared test or by numerical approximation of the Fisher exact test, when all expected frequencies were greater than 5 or not, respectively. The progression free survival and the overall survival were evaluated by Kaplan–Meier curves and to evaluate differences between two or more survival curves log-rank test was performed. Stepwise Cox regression was used to examine factors contributing to overall survival and the hazard ratio (HR) with their 95% confidence interval (CI) are estimated. A p-value <0.05 was considered statistically significant. Statistical analysis was performed using the software StatView for Windows version 5.0.1 (SAS Institute, Cary, NC, USA) and the SPSS Statistics version 22.0.
3
Physician Opinions and Practice Analysis
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StatView for Windows version 5.0.1® (SAS Institute, Inc., Cary, NC) and Stata Statistical Software, Release 8.0® (Stata Corporation, College Station, TX) were used. Univariate analyses were performed to identify factors associated with data regarding physicians’ opinions and practice.
Data were compared using univariate logistic regression (categorical variables) and Fischer or Chi-squared tests as suitable. Results are expressed as proportions, odds ratios (OR), with 95% confidence intervals (CIs), and P values as (nx/ny [%] vs nz/nw [%], OR [95% CI] P).
Results of the QOC are expressed for all items and the 2 subscores as mean (±SD). The 2 subscores were compared using a linear regression and significant differences were compared with other results using the unpaired T-test or ANOVA.
Data were compared using univariate logistic regression (categorical variables) and Fischer or Chi-squared tests as suitable. Results are expressed as proportions, odds ratios (OR), with 95% confidence intervals (CIs), and P values as (nx/ny [%] vs nz/nw [%], OR [95% CI] P).
Results of the QOC are expressed for all items and the 2 subscores as mean (±SD). The 2 subscores were compared using a linear regression and significant differences were compared with other results using the unpaired T-test or ANOVA.
4
Thyroid Dysfunction Risk Analysis in Cancer Patients
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For the statistical analysis, patients were divided into two groups: the overt thyroid dysfunction group (n = 11) and the non-thyroid dysfunction group (n = 61). The t test for independent data or the Mann–Whitney test was performed for normal or non-normal variables, respectively. To evaluate significant differences in data frequency, we analyzed contingency tables. Tables with size larger than 2 × 2 were examined by the Chi-squared test or a numerical approximation of the Fisher exact test, when all cell frequencies were greater than 4 or not, respectively. The following variables were studied by univariate and multivariate analysis: age, sex, cancer type, ultrasonographic thyroid features, drug administered, TSH, FT4, TgAbs and TPOAbs levels at baseline and length of follow-up. A receiver operating characteristic (ROC) curve was constructed to identify a baseline TSH cut-off associated with increased risk of overt thyroid dysfunction. Statistical analysis was performed using the software StatView for Windows version 5.0.1 (SAS Institute, Cary, NC) and the IBM SPSS Statistics version 22.0. A p value < 0.05 was considered statistically significant.
5
Haematological Impacts of Trypanosomiasis and Treatments
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Clinical and haematological data were analysed using Statview for Windows Version 5.0.1 (1995–1998; SAS Institute Inc., Cary, NC, USA). Repeated measures ANOVA, with Fisher’s PLSD post hoc test, was used to test the effect of trypanosome infection on haematologic parameters in comparison with respective baseline values (α = 0.05). In addition, the effects of IM DB829 and oral DB868 on these haematologic parameters in monkeys with confirmed second stage HAT were tested. Pharmacokinetic outcomes were recovered using traditional non-compartmental methods using Phoenix WinNonlin (version 6.2; Pharsight, Mountain View, CA, USA).
6
Soil Properties After Prescribed Burning
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In order to identify the differences in the studied soil properties surrogated to burning and post-fire elapsed time as well as soil depth, one-way ANOVA tests were used since the interaction between time and depth was significant. Sampling time (U, B0, B6, B12) was considered as fixed factor to analyse the effect of fire and time, splitting data by soil depth (0-1, 1-2 and 2-3 cm). Additionally, changes in soil properties with depth were checked using soil depth (0-1, 1-2 and 2-3 cm) as fixed factor, splitting data by sampling time (U, B0, B6, B12). All data met the assumptions of normality and homoscedasticity so no transformations were required. These statistical analyses were carried out using StatView for Windows version 5.0.1 (SAS Institute Inc, Cary, North Carolina, USA). Data presented in the text are reported as mean ± standard deviation of the mean unless otherwise stated.
7
Soil Properties Comparison After Fire
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To identify differences among the variables related to sampling time and soil depth, oneway ANOVA tests were performed because the interaction between time and depth was significant in most cases. The sampling time (U, B0, B6, B12, B18 and B24) was considered a categorical independent variable to analyze the effects of fire and time, and the data were split by soil depth (0-1, 1-2 and 2-3 cm). The variations in soil properties among soil depths were tested using soil depth as a categorical independent variable, and the data were split by sampling time (U, B0, B6, B12, B18 and B24). All the data met the assumptions of normality and homoscedasticity, and no further transformations were required. These analyses were performed using StatView for Windows version 5.0.1 (SAS Institute Inc. Cary, North Carolina, USA). A principal component analysis (PCA) was also performed to identify the relationships among the soil properties, using the Pearson correlation and a varimax rotation with Kaiser normalization (XLSTAT 2017.
Addinsoft, Paris, France). The values reported in the text are expressed as the mean ± standard deviation unless otherwise noted.
Addinsoft, Paris, France). The values reported in the text are expressed as the mean ± standard deviation unless otherwise noted.
8
Quantitative Analysis of DNA Methylation
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All statistical analyses were done using StatView for Windows version 5.0 (SAS Institute, Cary, NC, USA). All results are represented as mean ± SE. Two-way analysis of variance (ANOVA) was used to analyze the effect of treatment in the MSRE-qPCR and 5 hmC data across all time points. The MSRE-qPCR data after siRNA knockdown was analyzed by one-way ANOVA followed by Fisher’s PLSD post hoc test. Student’s t-test was employed for the rest of the data. P-value less than 0.05 were considered statistically significant.
9
Comparative Statistical Analysis Methodology
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All values are presented as means ± standard error (SE). Unpaired Student’s t-tests after a preliminary F-test of the homogeneity of within-group variance were used to compare values between groups. When more than two groups were compared, statistical analysis was conducted with one-way ANOVA followed by Fisher’s protected least significant difference or Dunnett’s post-hoc tests (Statview for Windows version 5.0, SAS Institute Inc., Cary, NC). A P value of less than 0.05 was considered statistically significant.
10
Statistical Analysis of t-Flavanone Effects
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All results of the human study were statistically analyzed using the Mann-Whitney U test to identify the differences between the placebo and t-flavanone groups. The results of the in vitro study were statistically analyzed using Dunnett’s test to identify the differences between the vehicle control and t-flavanone groups. In each case, StatView for Windows version 5.0 (SAS Institute, Inc., Cary, NC, USA) was used, and a P value <0.05 denoted the presence of a statistically significant difference.
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