Values are expressed as the median. Univariate analyses for continuous variables were undertaken using Student’s t-test, paired t-test, and one-way ANOVA. For the analysis of categorical variables, Mann–Whitney U test, Fisher’s exact test, chi-squared test, proportional hazard model test, and Gray’s test with log-rank test results were performed. A multivariate analysis was performed using the Cox proportional hazards model and was applied only to variables that were statistically p < 0.05 in the univariate analysis. A survival analysis was performed using the Kaplan–Meier method. Statistical analyses were performed using JMP statistical software, version 15.0 (Windows version, SAS Institute, Cary, NC, USA). All p-values were derived from two-tailed tests, with p < 0.05 accepted as statistically significant. ROC and area under the curve values were calculated to define cutoff values for risk factors of HCC-R.
Jmp statistical software version 15
Manufactured by SAS Institute
Sourced in United States
JMP statistical software, version 15, is a data analysis and visualization tool developed by SAS Institute. It enables users to explore, analyze, and model data using a variety of statistical methods and graphical representations.
Automatically generated - may contain errors
11 protocols using jmp statistical software version 15
1
Risk Factors for Hepatocellular Carcinoma Recurrence
2
Comparing Treatment Effects via ANOVA
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One‐way analysis of variance (ANOVA) and Tukey's honest significant difference (HSD) pairwise comparison tests were conducted using JMP statistical software version 15.0 (SAS Institute Inc.). Microsoft Office Excel was used to calculate means, standard deviations, and standard errors.
3
Regulatory Action Corroborated Findings
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Descriptive data are reported as number (percentage), median (interquartile range), and mean (standard deviation). The proportion of regulatory actions with and without corroborated findings within the published literature are reported with descriptive statistics. All statistical analyses were conducted in R 3.6.0 (R foundation for Statistical Computing, Vienna, Austria), JMP statistical software version 15.0.0 (SAS Institute), Python statistical software version 3.8 (Python), and Excel spreadsheet software version 16.16.27 (Microsoft).
4
Differences in Therapeutic Indication Approvals
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We used descriptive statistics to examine differences in trial characteristics and indication characteristics. Frequencies of use of randomization and double-blinding among the trials in our sample were calculated excluding single-group studies. We next used χ2 and Wilcoxon tests where appropriate to examine differences between supplemental indication approvals for therapeutics in our sample at multiple levels, including agent type, special regulatory pathway, and therapeutic area, all of which were defined before data collection. Analyses were performed using R statistical software version 3.4.3 (R Project for Statistical Computing), JMP statistical software version 15.0.0 (SAS Institute), Python statistical software version 3.8 (Python), and Excel spreadsheet software version 16.16.27 (Microsoft). All statistical tests were 2-tailed and used a type I error rate of .01 to account for multiple comparisons. Data analysis was performed from August to October 2020.
5
Statistical Analysis of Clinical Outcomes
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All statistical analyses were carried out using JMP statistical software, version 15 (SAS Institute Inc., Cary, NC, USA) and R, version 3.2.1 (R Foundation for Statistical Computing, Vienna, Austria). Continuous variables were expressed as the mean ± standard deviation and compared using the nonparametric Wilcoxon test for independent samples. The chi‐squared test was used to compare categorical values. A logistic regression analysis was applied to the multivariate analyses.5 , 25 Survival was calculated with the Kaplan‐Meier product‐limited method; differences in survival between the groups were compared with the log‐rank test. P‐values < 0.05 were considered statistically significant.
6
Statistical Analysis of Skin Allograft Survival
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Statistical analyses were performed using JMP statistical software, version 15 (SAS Institute, Cary, NC). The Chi-square test or Fisher’s exact test were used for the comparison of categorical variables and the Student’s t-test or the Mann–Whitney U-test for continuous variables. Differences in SI values before and after desensitization were tested using Wilcoxon signed rank test. Comparisons between groups were made using analysis of variance (ANOVA), and significant differences were examined with Tukey–Kramer multiple comparison post hoc test. Survival rates of the skin allografts were calculated using Kaplan–Meier/log-rank test. P-values below 0.05 were considered statistically significant.
7
Evaluating HSUV Changes in End-Stage Breast Cancer
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We determined the root-mean-squared error (RMSE) of the difference among the mean HSUVs of the aforementioned three groups 6 and 3 months before the last data collection date (or death). We examined the null hypothesis ‘absolute error of HSUVs is equal in the three groups’, using the one-way analysis of variance. A p-value < 0.05 (typically ≤0.05) was considered statistically significant. We performed multiple comparisons (Tukey’s test) to determine the group that differed from others, following the rejection of the null hypothesis. We studied the trend of HSUVs in patients with end-stage breast cancer 6 months before death, 3 months before death, and 0 at death using a parallel plot. Statistical analyses were performed using JMP statistical software version 15 (SAS Institute Inc., Cary, NC, USA).
8
Surgical Weight Loss Effects on Sleep Apnea
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Data are presented as numbers and percentages for categorical variables, and as means ± standard deviations for continuous variables. Statistical analysis was carried out using χ2‐tests for categorical variables, and Student’s t‐tests or Mann–Whitney U‐tests for continuous variables. We used paired t‐tests or Wilcoxon tests for continuous variables to enable the comparison of all parameters between pre‐ and postoperative measures. We used Spearman’s rank correlation coefficient to investigate the relationships between the weight loss effects and changes in anatomical parameters. Potential factors for decreasing AHI were then analyzed using univariate and multivariate analyses, and a logistic regression model with a stepwise method (forward–backward method). Factors with a P‐value <0.1 in univariate analysis were subsequently entered into the logistic regression analysis along with the Akaike information criterion. P‐values <0.05 were considered statistically significant. All statistical analyses were carried out using JMP statistical software, version 15 (SAS Institute, Cary, NC, USA).
9
Survival Analysis of Cancer Outcomes
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Categorical variables are shown as the number and percentage and were compared using Fisher's exact test. Five‐year OS and RFS were estimated using the Kaplan–Meier method and compared using the log‐rank test. Risk factors for complications and their effect on 5‐year RFS were identified using univariable and multivariable Cox proportional hazards models. Clinical factors with the potential to have a confounding effect on 5‐year RFS were adjusted for in the multivariable model. All statistical analyses were performed using JMP statistical software version 15 (SAS Institute Inc.). All p‐values were two‐sided and those less than 0.05 were considered statistically significant.
10
Sleep, Alcohol, and Mental Health Assessment
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Statistical analysis and data discovery were carried out using the JMP™ version 15 statistical software (SAS Headquarters, Cary, NC) and the IBM SPSS Statistics software. Graphical figures were generated in the JMP™ Statistical Discovery version 15 software. Student's or Welch's t-tests and Chi-square tests were used to assess differences in sleep quality (PSQI), CPRS anxiety (BSA) and Depression (MADRS) between the AUD group and the healthy controls. Pearson correlation was used to assess sleep measures (PSQI) and alcohol consumption variables within each group. To assess sex specific differences, PSQI measures and clinical diagnoses of mental health disorders including anxiety and mood disorders (SCID-IV/5) were compared between sexes in the AUD group only using a Student's t-test. Differences in liver and inflammation biomarkers values were assessed between the AUD population and the healthy controls using a Wilcoxon ranked sum test. Data are presented as mean ± standard deviation.
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