Statview version 5.0 for windows

Manufactured by SAS Institute

Sourced in United States

Statview version 5.0 for Windows is a statistical analysis software package developed by SAS Institute. It provides a comprehensive set of tools for data analysis, including descriptive statistics, regression analysis, ANOVA, and more. The software is designed to run on the Windows operating system.

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7 protocols using statview version 5.0 for windows

Factors Affecting CRT Device Selection

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All data are expressed as mean±SD. Simple between-group analysis was conducted using Student׳s t-test. Categorical variables were compared using Fisher׳s exact test. Logistic regression analysis was used to estimate the factors affecting the choice of CRT-D vs. CRT-P. Differences with P<0.05 were considered significant. Statview version 5.0 for Windows (SAS Institute Inc., Cary, NC, USA) was used for all statistical analyses.

Yokoshiki H., Shimizu A., Mitsuhashi T., Furushima H., Sekiguchi Y., Manaka T., Nishii N., Ueyama T., Morita N., Nitta T, & Okumura K. (2016). Trends and determinant factors in the use of cardiac resynchronization therapy devices in Japan: Analysis of the Japan cardiac device treatment registry database. Journal of Arrhythmia, 32(6), 486-490.

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Cardiac Resynchronization Therapy Outcomes

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All data are expressed as mean ± SD. Simple between‐group analysis was conducted using Student's t‐test. Categorical variables were compared using the chi‐squared test or Fisher's exact test. The Kaplan–Meier curves were constructed to estimate event‐free outcomes in the study groups with comparison using the log‐rank test. Hazard ratios for events in CRT‐D patients of the New JCDTR group versus the JCDTR group were computed with a multivariate Cox proportional‐hazards regression model after adjusting for confounding factors including age, gender, indication, etiology, LVEF, NYHA class, QRS width, and hemoglobin and creatinine levels. Differences with p < .05 were considered significant. StatView version 5.0 for Windows (SAS Institute Inc., Cary, NC, USA) or R software ver.3.6.3 (https://www.r‐project.org/) was used for all statistical analyses.

Yokoshiki H., Shimizu A., Mitsuhashi T., Ishibashi K., Kabutoya T., Yoshiga Y., Kondo Y., Abe H, & Shimizu W. (2023). Improved outcomes of cardiac resynchronization therapy with a defibrillator in systolic heart failure: Analysis of the Japan cardiac device treatment registry database. Journal of Arrhythmia, 40(1), 30-37.

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Statistical Analysis of Continuous and Categorical Data

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Data were analyzed using Stat view version 5.0 for windows (SAS Institute, Inc., IL, USA). Continuous variables were presented as mean ± standard deviation and/or as median (inter-quartile range). Categorical variable were expressed as percentages. Chi-squared test was used to compare categorical data and t-test or Mann–Whitney test to compare continuous data. A p values <.05 was considered significant.

Halle M.P., Chipekam N.M., Beyiha G., Fouda H., Coulibaly A., Hentchoya R., Kaze F.F., Luma N.H, & Ashuntantang G. (2017). Incidence, characteristics and prognosis of acute kidney injury in Cameroon: a prospective study at the Douala General Hospital. Renal Failure, 40(1), 30-37.

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Predictors of Clinical Outcomes

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All data are expressed as mean ± SD. Simple between‐group analysis was conducted using Student's t‐test. Categorical variables were compared using Fisher's exact test. Kaplan–Meier curves were constructed to estimate event‐free outcomes in the two study groups with comparison using the log‐rank test. A multivariate Cox proportional‐hazards regression model was used to estimate hazard ratios for clinical events. Among the variables that reached a significance level of P < .1 in univariate models, a stepwise selection was used to determine the most agreeable model. Differences with P < .05 were considered significant. Statview version 5.0 for Windows (SAS Institute Inc, Cary, NC, USA) or R software ver.3.2.3 (https://www.r-project.org/) was used for all statistical analyses.

Yokoshiki H., Shimizu A., Mitsuhashi T., Furushima H., Sekiguchi Y., Manaka T., Nishii N., Ueyama T., Morita N., Okamura H., Nitta T., Hirao K, & Okumura K. (2018). Prognostic significance of nonsustained ventricular tachycardia in patients receiving cardiac resynchronization therapy for primary prevention: Analysis of the Japan cardiac device treatment registry database. Journal of Arrhythmia, 34(2), 139-147.

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Factors Predicting Appropriate ICD Therapy

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All data are expressed as mean ± SD. Simple between‐group analysis was conducted using Student's t‐test. Categorical variables were compared using Fisher's exact test. Kaplan‐Meier curves were constructed to estimate event‐free outcomes in the study groups with comparison using the log‐rank test. Hazard ratios were computed with a multivariate Cox proportional‐hazards regression model after adjusting for confounding factors including age, gender, LVEF, and NYHA class. All reliable variables associated with appropriate ICD therapy (p < .10) were entered into a multivariable model using forward stepwise regression analysis. Differences with p < .05 were considered significant. Statview version 5.0 for Windows (SAS Institute Inc.) or R software ver.3.6.3 (https://www.r‐project.org/) was used for all statistical analyses.

Yokoshiki H., Shimizu A., Mitsuhashi T., Ishibashi K., Kabutoya T., Yoshiga Y., Kondo Y., Abe H, & Shimizu W. (2023). Cardiac resynchronization therapy with a defibrillator in non‐ischemic and ischemic patients for primary and secondary prevention of sudden cardiac death: Analysis of the Japan cardiac device treatment registry database. Journal of Arrhythmia, 39(5), 757-765.

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Predictors of Appropriate Defibrillator Therapy in Patients with CIEDs

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All data are expressed as mean ± SD. Simple between‐group analysis was conducted using Student's t‐test. Categorical variables were compared using Fisher's exact test. Kaplan‐Meier curves were constructed to estimate event‐free outcomes in the two study groups with comparison using the log‐rank test. A logistic regression analysis was used to estimate the factors associated with performing EPS before CIEDs implant. Among the variables that reached a significance level of P < .1 in univariate models, multivariate analysis was performed. In patients who underwent EPS, a multivariate Cox proportional‐hazards regression model with a stepwise selection was used to estimate significant factors for appropriate defibrillator therapy. The sensitivity and specificity of BNP levels for the prediction of appropriate defibrillator therapy were evaluated using receiver operating characteristic (ROC) curve. Differences with P < .05 were considered significant. Statview version 5.0 for Windows (SAS Institute Inc) or R software ver.3.6.3 (https://www.r‐project.org/) was used for all statistical analyses.

Yokoshiki H., Shimizu A., Mitsuhashi T., Ishibashi K., Kabutoya T., Yoshiga Y., Kohno R., Abe H, & Nogami A. (2020). Current status and role of programmed ventricular stimulation in patients without sustained ventricular arrhythmias and reduced ejection fraction: Analysis of the Japan cardiac device treatment registry database. Journal of Arrhythmia, 37(1), 148-156.

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Analyzing Biomedical Data Distributions

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The quantitative variables with normal distribution are presented as a mean and SD. The other variables (qualitative) are represented as a percentage. Statview version 5.0 for Windows (SAS Institute Inc. Cary, NC, USA) was used for statistical analysis.

Boles U., Gul E.E., Fitzgerald L., Sadiq Ali F., Nolan C., Aldworth-Gaumond K., Redfearn D.R., Baranchuk A., Glover B., Simpson C., Abdollah H, & Michael K.A. (2017). Standardized programming to reduce the burden of inappropriate therapies in implantable cardioverter defibrillators - Single centre follow up results. Indian Pacing and Electrophysiology Journal, 18(2), 56-60.

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