We conducted a statistical analysis using Pearson's correlation coefficient and multiple regression analysis to investigate the relationships between clinical outcome and patients' baseline characteristics, including tolvaptan concentration, as well as the relationships among PK parameters. Specifically, we utilized Pearson's correlation coefficient to assess the linear relationship between two continuous variables and to evaluate the strength and direction of the association between our variables of interest. Given that our variables were measured on a continuous scale, this test was selected for our analysis. Multiple regression analysis was performed to determine which covariates significantly influenced the clinical outcome. The method used to select the variables in the model was a stepwise selection. The significance level of the score of entering an effect into the model was .20. Independent predictive variables with p values of less than .05 were considered statistically significant. Overall, the selection of these statistical tests was based on the nature of our research questions and the types of variables we were examining. Data were analyzed using JMP version 5.0.1a (SAS).
Jmp version 5.0.1a
JMP version 5.0.1a is a data analysis software product developed by SAS Institute. It provides users with tools for data visualization, exploration, and statistical analysis. The core function of JMP is to enable data-driven decision making through intuitive graphical interfaces and a wide range of analytical capabilities.
Lab products found in correlation
4 protocols using jmp version 5.0.1a
Exploring Tolvaptan Pharmacokinetics and Outcomes
We conducted a statistical analysis using Pearson's correlation coefficient and multiple regression analysis to investigate the relationships between clinical outcome and patients' baseline characteristics, including tolvaptan concentration, as well as the relationships among PK parameters. Specifically, we utilized Pearson's correlation coefficient to assess the linear relationship between two continuous variables and to evaluate the strength and direction of the association between our variables of interest. Given that our variables were measured on a continuous scale, this test was selected for our analysis. Multiple regression analysis was performed to determine which covariates significantly influenced the clinical outcome. The method used to select the variables in the model was a stepwise selection. The significance level of the score of entering an effect into the model was .20. Independent predictive variables with p values of less than .05 were considered statistically significant. Overall, the selection of these statistical tests was based on the nature of our research questions and the types of variables we were examining. Data were analyzed using JMP version 5.0.1a (SAS).
Parkinson's Disease Neuroimaging Analysis
Statistical Analysis of Ordinal Data
Irinotecan Efficacy by Tumor Type
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