To answer the primary research question, bivariate analyses included a correlation matrix to describe the relationship between recall of parental feedback in this ED-specific population and ANOVAs were run to assess the subscales by different current diagnosed EDs. Multivariable linear regressions were run using stepwise regression with EPSI total scores to determine whether direct (e.g., encouragement to diet, criticism of weight) or indirect (e.g., parental dieting or fat talk) feedback corresponded with higher scores. We used the total score since we were interested in total eating pathology, rather than particular subscales. We controlled for potential confounders such as parental eating disorder history to account for some genetic influences and we also controlled for body mass index (BMI), which could confound many of the subscales on the EPSI. There were not enough male patients to conduct subgroup analyses. Data analysis was conducted using SAS JMP® 13.0 and α was set equal to 0.05 for all analyses.
Sas jmp 13
Manufactured by SAS Institute
Sourced in United States
SAS JMP® 13.0 is a statistical discovery software that provides visual data analysis and modeling capabilities. It enables users to explore data, uncover patterns, and build predictive models.
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Lab products found in correlation
6 protocols using sas jmp 13
1
Parental Feedback and Eating Pathology
2
Randomized Experimental Data Analysis
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Experiments were conducted in randomized plots design with three replications. Experimental data were subjected to analysis of variance with the use of SAS-JMP 13.0 statistical software and significant means were compared with the use of LSD multiple comparison test.
3
Survival Analysis of Clinicopathological Factors
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All p-values were calculated 2-sided, and values of <0.05 were considered to be significant. Survival analyses were performed by univariate Kaplan–Meier regressions and tested for significance with the log-rank. Results were considered to be significant if the test revealed significance levels <0.05. Multivariate analyses were performed by Cox proportional hazard regression model, including all relevant clinicopathological characteristics (pT-Stage, pN-Stage, lymphovascular invasion (L), blood vessel invasion (V), age, gender, receipt of adjuvant platinum-containing chemotherapy, status of resection margins, and tumor grading (WHO 2016 and WHO 1973)). Statistical analyses of numeric continuous variables were performed by nonparametric tests (Wilcoxon rank-sum test, Kruskal–Wallis test). Contingency analysis of nominal variables was performed by Pearson’s chi-squared test. Correlation analysis of numeric continuous variables was performed using Spearman rank correlations. All statistical analyses were performed with GraphPad Prism 7.2 (GraphPad Software Inc., La Jolla, CA, USA) and JMP SAS 13.2 (SAS, Cary, NC, USA).
4
Survival Analysis of Bladder Cancer
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Cluster analysis was performed by unsupervised hierarchical clustering based on Ward clustering method using Euclidean distance as metric scale. Disease-specific (DSS) and recurrence-free survival (RFS) curves were estimated by the Kaplan–Meier method. Log-rank test was used for time-to-event outcomes. Multivariable survival analyses were conducted using Cox proportional hazards regression modeling to assess the magnitude of impact while adjusting for well-known clinico-pathological risk parameters (gender, age, resection margin, WHO 1973 Grading, WHO 2016 Grading, lymphovascular invasion, blood vessel invasion, perineural sheath invasion, receipt of adjuvant platinum-based chemotherapy, variant histology). All P values were two-sided, and a P value < 0.05 was considered statistically significant. All statistical analyses were performed by GraphPad Prism 8.3.0 (GraphPad Software Inc., La Jolla, CA, USA) and JMP SAS 13.2 (SAS, Cary, NC, USA).
5
Statistical Analysis of Experimental Findings
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All statistical analyses were performed using SAS JMP13 (SAS Institute-9.3, Cary, NC, United States). The results are presented as the mean ± standard error from triplicate experiments, and data were analyzed using Student’s t-test for comparison of two means or one-way analysis of variance followed by Tukey’s test. The level of significance was set at p < 0.05 for all treatments. All the graphs were created using Prism 6.0 for windows (GraphPad, La Jolla, CA, United States)3 .
6
Thrombocyte Count Dynamics Analysis
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Data are medians with 25th–75th centiles if not stated otherwise. Linear mixed‐effects multiple regression (Fit Model platform, Method REML, SAS‐JMP13, SAS Institute, NC, USA) was used to analyse the increase in thrombocyte count with time, using subject identity as random effect to account for correlation between repeated observations within the same subject.
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