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Jmp version 12 for windows

Manufactured by SAS Institute
Sourced in United States, Japan

JMP version 12 for Windows is a data analysis software that provides a comprehensive suite of statistical tools and visualization capabilities. It offers a user-friendly interface for data exploration, modeling, and reporting. The core function of JMP 12 is to assist users in analyzing and interpreting complex data sets.

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13 protocols using jmp version 12 for windows

1

Multivariate Analysis of miRNA Biomarkers

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Continuous variables including laboratory data and categorical data including age <65 or >65 years, sex, BMI <22.0 or >22.0 kg/m2, primary cancer site, PS, and stage were calculated using the t-test or chi-square test, respectively. Linear discriminant analysis and model selection based on leave-one-out cross-validation were performed using R version 3.1.2 (R Foundation for Statistical Computing, http://www.R-project.org), compute.es package version 0.2-4, hash package version 2.2.6, MASS package version 7.3-45, mutoss package version 0.1-10, and pROC package version 1.8. AUCs for the construct combining miRNA discriminants and the clinical model using the DeLong test were analyzed using JMP for Windows version 12.0 (SAS Institute, Cary, NC, USA). The level of significance was set as p < 0.05.
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2

Impacts of Persistent and Changing Renal Impairment Rates

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Categorical data are presented as numbers and percentages and compared using the chi-square test. Continuous variables are expressed as mean ± standard deviation or as median and interquartile range and were compared using one-way analysis of variance or the Kruskal–Wallis test. We applied analysis of variance (ANOVA) to persistent high RIR, increased RIR, attenuated RIR and persistent low RIR, then post-hoc pairwise comparisons between each group were conducted with Bonferroni corrections. Unadjusted cumulative event rates were estimated using Kaplan–Meier curves and were compared among the four groups. Multivariate analysis included clinically important variables, such as age, sex, hypertension, chronic kidney disease (CKD), diabetes mellitus (DM), dyslipidemia (DL), body mass index (BMI), smoking status, multivessel disease, left ventricular ejection fraction (LVEF), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), use of statins and RIR. Values of p < 0.05 were considered to indicate statistical significance, unless otherwise indicated. All data were analyzed using JMP for Windows version 12.0 (SAS Institute, Cary, NC, USA).
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3

ECG-LVH Impact on Clinical Outcomes

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Patient characteristics and treatment were compared using the Pearson chi-squared test for categorical variables, Student’s t-test for normally distributed continuous variables, and the Wilcoxon test for non-normally distributed continuous variables. The correlations between ECG-LVH and other variables were evaluated with a univariate logistic model. Cumulative event-free rates during follow-up were derived using the Kaplan-Meier method. The relationship between the presence of ECG-LVH at baseline and the outcomes was evaluated on multivariable adjustment. Clinical variables derived from a univariate Cox proportional hazard analysis were used in a multivariate Cox proportional hazard models. P<0.05 was used as the criterion for variables to stay in the model. JMP for Windows version 12 (SAS Institute, Cary, NC, USA) was used for all statistical analyses.
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4

Statistical Analysis of Research Data

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Descriptive statistics (mean ± SD, coefficient of variation [CV], and ICC) were calculated using a commercially available statistical software program (SPSS for Windows, version 25, IBM/SPSS, Chicago, IL, USA) and JMP (for Windows, version 12 SAS Institute Inc., Cary, NC, USA).
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5

Statistical Analysis of Group Differences

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Comparisons between two groups were performed using the t-test. Differences in survival between groups were analyzed by the log-rank test. All statistical analyses were conducted using the JMP for Windows version 12.0 software package (SAS Institute Inc., Cary, NC, USA).
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6

Survival Prediction Using Immunological Factors

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A ROC curve was used to determine the cutoff values of continuous variables such as the numbers of CD4+, CD8+ or Foxp3+ T lymphocytes or the H-scores of EMT-related proteins or PD-L1 (Supplementary Figure 1). The values of the percentages of tumor cells with recognized staining alteration as a continuous variable, and survival (alive or dead at the median follow-up time) as a binary variable were subjected to ROC analysis, as previously described [30 (link)].
The correlation of each factor such as the tumor infiltration of CD4+, CD8+ or Fop3+ lymphocytes, PD-L1 expression, EMT-related protein expression and clinicopathological factors was analyzed using the Pearson Chi-squared method. Survival was estimated with the Kaplan-Meier method, and survival estimates were compared using the log-rank test. Overall survival was calculated from the date of surgery to the date of death from any cause or last contact/follow-up. Multivariate analysis was conducted using Cox proportional hazards regression modeling. Baseline variables with P < 0.05 in univariate analysis were included in multivariate models. The threshold for significance was P < 0.05. All statistical analyses were conducted using the JMP for Windows version 12.0 software package (SAS Institute, Cary, NC).
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7

Statistical Analysis of Clinicopathologic Variables

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Data were analyzed as described using GraphPad Prism version 6 for Windows (GraphPad Software, La Jolla, CA USA) or JMP version 12 for Windows (SAS Institute, Inc., Cary, NC USA). For all statistics, p<0.05 was used for statistical significance. For clinicopathologic variables, normally distributed variables were compared with 2-tailed t-test and non-normally distributed variables with the Mann-Whitney U test. Fisher's Exact test was used for mean comparisons between >2 groups. Chi-Square test was used to compare proportions.
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8

Statistical Analysis of Correlations

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The statistical analyses were performed using Microsoft Excel 2016 (Microsoft, Washington, DC, USA) and JMP version 12 for Windows (SAS Institute Inc, Tokyo, Japan). Chi‐squared tests and the Mann‐Whitney U test were performed. The odds ratios (ORs) and 95% confidence intervals (CIs) were estimated to determine the strength of these correlations. P values <0.05 were considered significant.
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9

Retrospective Analysis of Complications in TLH

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This study was reviewed and approved by the Human Ethical Committee of the University of Teikyo Hospital (Trial registration number: 20-094). The de-identified medical records of 323 female patients who underwent TLH from June 1, 2015 to December 31, 2019 were reviewed retrospectively. In these cases, bilateral salpingectomy (BS) or BSO was performed during TLH. This study also included 25 cases with concomitant PLA and 19 cases with concomitant LC, including 2 bilateral LCs and 17 unilateral LCs. Among the 323 cases, we extracted representative patients’ characteristics and surgical complications as described below. Statistical analyses were performed using JMP version 12 for Windows (SAS Institute, Inc., Tokyo, Japan) to determine the correlations between patient characteristics and surgical complications. The odds ratios (ORs) and 95% confidence intervals (CIs) were estimated to determine the strengths of the correlations. P < 0.05 was considered statistically significant.
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10

Mortality Risk in Riser Patients

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Continuous variables were expressed as mean ± standard deviation or median (interquartile range), and inter‐group differences were compared using Student's t‐test. Categorical variables were summarized as percentages and analysed using the χ2 test. A Cox proportional hazards model was used to investigate the hazard ratio (HR) for all‐cause and cardiovascular deaths. Results were reported as HR, 95% confidence interval (CI), and P values. The HR for outcomes in the riser group was compared with that for the non‐riser group, which served as the reference group. JMP version 12 for Windows (SAS Institute Inc., Cary, NC) was used for all statistical analyses. P values <0.05 were considered statistically significant.
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