Jmp software for windows version 11

Manufactured by SAS Institute

Sourced in Japan, United States

JMP software for Windows, version 11.0, is a data analysis and statistical modeling software package developed by SAS Institute. It provides tools for data exploration, visualization, and statistical analysis. The software is designed to run on the Windows operating system.

Automatically generated - may contain errors

Lab products found in correlation

Spelling variants of this product

JMP software (1534 citations) JMP 11 (758 citations) JMP version 11 (334 citations) JMP software version 11 (88 citations) JMP software for Windows (15 citations) JMP Version 11 for Windows (3 citations) JMP 11 for Windows (3 citations)

10 protocols using jmp software for windows version 11

Clinicopathological Factors and Breast Cancer Outcomes

Check if the same lab product or an alternative is used in the 5 most similar protocols

We evaluated the relationship between the clinicopathological factors and clinical outcomes. Locoregional recurrence-free survival (LRFS) includes ipsilateral invasive or noninvasive chest wall tumor recurrence (ie, breast cancer that involves the same parenchyma as the original primary) and regional breast cancer recurrence (ie, invasive or noninvasive breast cancer in regional lymph nodes including the ipsilateral axilla, supra- or infra-clavicular, or internal mammary lymph nodes). Distant metastasis-free survival (DMFS) includes the events of metastatic disease-breast cancer that has either been biopsy-confirmed or clinically diagnosed as recurrent breast cancer. Overall survival (OS) includes death that is attributable to any cause including breast cancer, nonbreast cancer, or unknown cause. LRFS and DMFS were calculated from the date of chemotherapy initiation to the date of the documented initial recurrence. Observations were censored on the date that the patient was last known to be alive and LRFS, DMFS, and OS were estimated with the Kaplan-Meier method. Differences were compared using the log-rank test. Univariate and multivariate Cox regression analyses were used to determine the prognostic factors.
JMP software version 11 for Windows (SAS Institute, Cary, NC) was used for statistical analyses. Two-sided P-values of < .05 were considered statistically significant.

Nakajima N., Oguchi M., Kumai Y., Yoshida M., Inoda H., Yoshioka Y., Iwase T., Ito Y., Akiyama F, & Ohno S. (2018). Clinical outcomes and prognostic factors in patients with stage II-III breast cancer treated with neoadjuvant chemotherapy followed by surgery and postmastectomy radiation therapy in the modern treatment era. Advances in Radiation Oncology, 3(3), 271-279.

+ Open protocol

+ Expand

APOBEC3B mRNA Expression and pCR Prediction

Check if the same lab product or an alternative is used in the 5 most similar protocols

The nonparametric Wilcoxon test was adopted for statistical analysis of association between APOBEC3B mRNA expression and pCR status. The best cut-off point of pCR for APOBEC3B mRNA expression levels was determined through a receiver operating characteristics (ROC) curve and used for classification of APOBEC3B mRNA expression. The association between APOBEC3B mRNA expression status and clinicopathological factors was evaluated using Chi-square or Fisher’s exact test. Logistic regression methods were also adopted for univariate and multivariate analyses to assess the associations of clinical and biological parameters with pCR. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated. Relapse-free survival and breast cancer–specific survival curves were calculated according to the Kaplan–Meier method and verified by the log-rank test. A statistically significant difference was defined at P < 0.05. All statistical analyses were performed using JMP software version 11 for Windows (SAS Institute Japan, Tokyo, Japan).

Fujiki Y., Yamamoto Y., Sueta A., Yamamoto-Ibusuki M., Goto-Yamaguchi L., Tomiguchi M., Takeshita T, & Iwase H. (2018). APOBEC3B gene expression as a novel predictive factor for pathological complete response to neoadjuvant chemotherapy in breast cancer. Oncotarget, 9(55), 30513-30526.

+ Open protocol

+ Expand

FGFR1 Gene Expression in Breast Cancer

Check if the same lab product or an alternative is used in the 5 most similar protocols

The associations of FGFR1 gene copy number, mRNA and protein expression levels with clinicopathological factors were analyzed using the χ²‐test. Relapse‐free survival (RFS) and breast cancer‐specific survival (BCSS) curves were calculated according to the Kaplan–Meier method and verified by the log‐rank test. Univariate and multivariate analyses of prognostic values were performed using Cox's proportional hazard model. A statistically significant difference was defined as P < 0.05. We used JMP software version 11 for Windows (SAS Institute Japan, Tokyo, Japan) for all statistical analyses.

Tomiguchi M., Yamamoto Y., Yamamoto‐Ibusuki M., Goto‐Yamaguchi L., Fujiki Y., Fujiwara S., Sueta A., Hayashi M., Takeshita T., Inao T, & Iwase H. (2016). Fibroblast growth factor receptor‐1 protein expression is associated with prognosis in estrogen receptor‐positive/human epidermal growth factor receptor‐2‐negative primary breast cancer. Cancer Science, 107(4), 491-498.

+ Open protocol

+ Expand

Statistical Analysis of Survival Outcomes

Check if the same lab product or an alternative is used in the 5 most similar protocols

Categorical and continuous variables were analyzed using Fisher's exact test and Welch's t‐test, respectively. A Cox proportional hazards model with stepwise regression was used to identify factors that predicted PFS and OS, and the results were described as hazard ratios (HRs) and 95% confidence intervals (CIs). PFS and OS were compared using the log‐rank test. Differences were considered statistically significant at a two‐tailed p ≤ 0.05. All analyses were conducted using the JMP software for Windows, version 11.0 (SAS Institute).

Imai H., Kishikawa T., Minemura H., Yamada Y., Ibe T., Yamaguchi O., Mouri A., Hamamoto Y., Kanazawa K., Kasai T., Kaira K., Kaburagi T., Minato K., Kobayashi K, & Kagamu H. (2021). Pretreatment Glasgow prognostic score predicts survival among patients with high PD‐L1 expression administered first‐line pembrolizumab monotherapy for non‐small cell lung cancer. Cancer Medicine, 10(20), 6971-6984.

+ Open protocol

+ Expand

Survival Analysis of Clinical Outcomes

Check if the same lab product or an alternative is used in the 5 most similar protocols

For statistical analysis, we used Fisher's exact test and Welch's t‐test for categorical and continuous variables, respectively. Cox proportional hazards models with stepwise regression were applied to identify factors predicting PFS and OS, and the results are presented as hazard ratios (HRs) and 95% confidence intervals (CIs). The Kaplan–Meier method was used to estimate survival as a function of time, and survival differences were analyzed using the log‐rank test. We performed univariate and multivariate logistic regression analyses according to the different outcome variables. The statistical significance level was set at a p‐value ≤0.05. All statistical analyses were performed using the JMP software for Windows, version 11.0 (SAS Institute).

Imai H., Naito E., Yamaguchi O., Hashimoto K., Iemura H., Miura Y., Shiono A., Mouri A., Kaira K., Kobayashi K, & Kagamu H. (2022). Pretreatment body mass index predicts survival among patients administered nivolumab monotherapy for pretreated non‐small cell lung cancer. Thoracic Cancer, 13(10), 1479-1489.

+ Open protocol

+ Expand

Multivariate Analysis of Efficacy Factors

Check if the same lab product or an alternative is used in the 5 most similar protocols

Categorical variables were analyzed using Fisher's exact test. The Wilcoxon signed‐rank test was used to check normality and equal variances and test for correspondence between the two groups. Multivariate ordered logistic regression analysis was used to identify factors that predicted efficacy, and the results are expressed as odds ratios (ORs) and 95% confidence intervals (CIs). Differences were considered statistically significant at a two‐tailed p‐value ≤0.05. All analyses were conducted using JMP software for Windows, version 11.0 (SAS Institute).

Imai H., Fujita Y., Hiruta E., Masuno T., Yamazaki S., Tanaka H., Kamiya T., Sandoh M., Takei S., Arai K., Nishiba H., Mogi J., Kaira K, & Minato K. (2022). A retrospective study of the efficacy and safety of naldemedine for opioid‐induced constipation in thoracic cancer patients. Thoracic Cancer, 13(16), 2301-2308.

+ Open protocol

+ Expand

Survival Analysis of Treatment Outcomes

Check if the same lab product or an alternative is used in the 5 most similar protocols

We adopted Fisher’s exact test and Welch’s t-test for categorical and continuous variables, respectively. We applied the Cox proportional hazards model with a stepwise regression procedure to identify factors that predicted PFS and OS. Hazard ratios (HR) and 95% confidence intervals (CI) were estimated. Univariate and multivariate logistic regression analyses were performed based on the different outcome variables. The Kaplan–Meier method was used to estimate survival as a function of time, and survival differences were analyzed using the log-rank test. Statistical significance was set at a two-tailed p-value ≤0.05. All statistical analyses were conducted using the JMP software for Windows, version 11.0 (SAS Institute, Cary, NC, USA).

Wasamoto S., Imai H., Tsuda T., Nagai Y., Minemura H., Yamada Y., Umeda Y., Kishikawa T., Shiono A., Kozu Y., Shiihara J., Yamaguchi O., Mouri A., Kaira K., Kanazawa K., Taniguchi H., Kaburagi T., Minato K, & Kagamu H. (2023). Pretreatment glasgow prognostic score predicts survival among patients administered first-line atezolizumab plus carboplatin and etoposide for small cell lung cancer. Frontiers in Oncology, 12, 1080729.

+ Open protocol

+ Expand

Survival Analysis of Tumor Relapse

Check if the same lab product or an alternative is used in the 5 most similar protocols

We defined RFS as the time from operation to the first instance of relapse or death from any reason. Overall survival was defined as the time from operation until death or censoring at the last consultation. We defined PPS as the time from tumor relapse after operation until death or censoring at the last consultation. Survival curves were generated using the Kaplan–Meier method. Linear regression analyses and Spearman’s rank correlation coefficient were adopted to assess whether RFS and/or PPS were associated with OS. A Cox proportional hazards model with stepwise regression was adopted to identify factors that predicted PPS and to estimate hazard ratios and 95% confidence intervals. Some variables were converted to an appropriate scale unit because the hazard ratio was calculated based on a 1-unit difference. Differences were regarded as statistically significant at a two-tailed p-value of ≤0.05. All analyses were conducted using JMP software for Windows, version 11.0 (SAS Institute, Cary, NC, USA).

Imai H., Onozato R., Ginnan M., Kobayashi D., Kaira K, & Minato K. (2021). Post-Progression Survival Is Strongly Associated with Overall Survival in Patients Exhibiting Postoperative Relapse of Non-Small-Cell Lung Cancer Harboring Sensitizing EGFR Mutations. Medicina, 57(5), 508.

+ Open protocol

+ Expand

Prognostic Factors in Cancer Treatment

Check if the same lab product or an alternative is used in the 5 most similar protocols

Categorical and continuous variables were analyzed using Fisher's exact test and Welch's t‐test, respectively, in subgroups defined by the GP score, NL ratio, and BMI. Cox proportional hazard models with stepwise regression were used to evaluate factors predicting PFS and OS. The hazard ratios (HRs) with 95% confidence intervals (CIs) were calculated. Univariate and multivariable logistic regression analyses were undertaken for different outcomes. Kaplan–Meier survival analysis was performed to estimate survival, whereas survival was evaluated using the log‐rank test. Two‐tailed p < 0.05 indicated statistical significance. Statistical analyses were performed using JMP software for Windows, version 11.0 (SAS Institute).

Imai H., Wasamoto S., Tsuda T., Nagai Y., Kishikawa T., Masubuchi K., Osaki T., Miura Y., Umeda Y., Ono A., Minemura H., Yamada Y., Nakagawa J., Kozu Y., Taniguchi H., Ohta H., Kasai T., Kaira K, & Kagamu H. (2023). Using the neutrophil‐to‐lymphocyte ratio to predict the outcome of individuals with nonsquamous non‐small cell lung cancer receiving pembrolizumab plus platinum and pemetrexed. Thoracic Cancer, 14(25), 2567-2578.

+ Open protocol

+ Expand

Evaluating Factors Impacting Survival Outcomes

Check if the same lab product or an alternative is used in the 5 most similar protocols

Linear regression analysis and Spearman's rank correlation coefficients were used to evaluate the association of PFS and/or PPS with OS. A Cox proportional hazards model with stepwise regression was used to identify factors that predicted PPS. The results were expressed as hazard ratios with 95% confidence intervals. Because the hazard ratio was defined for a 1‐unit difference, some factors were converted to an appropriate scale unit. PPS was compared using the log‐rank test. All statistical analyses were conducted using JMP software for Windows (version 11.0) (SAS Institute). Two‐tailed p‐values of ≤0.05 were considered statistically significant.

Imai H., Yamaguchi O., Mori K., Hashimoto K., Akagami T., Shinomiya S., Miura Y., Shiono A., Mouri A., Kaira K., Kobayashi K, & Kagamu H. (2021). Clinical impact of post‐progression survival on overall survival in patients receiving nivolumab monotherapy as a second‐line treatment for advanced non‐small cell lung cancer. Thoracic Cancer, 12(8), 1171-1179.

+ Open protocol

+ Expand

About PubCompare

Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.

We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.

However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.

Ready to get started?

Revolutionizing how scientists
search and build protocols!