Linear mixed models were employed for statistical analyses of the present data. The restricted maximum likelihood was used as the estimation method. The condition was modeled as a fixed effect, and the participant was modeled as a random effect. A variance component was used for the covariance structure of the random effect. For post-hoc analysis, the data were calculated as values subtracted from the Cont condition. Differences from zero in the NAME, Inv-NAME, and BEEP conditions were tested using paired one sample t-tests. Paired t-tests were employed to compare the self-relevance scores of the stimuli of the three conditions. All statistical analyses were performed using the SPSS system for Windows, version 22.0.
Spss system for windows version 22
Manufactured by IBM
Sourced in United States
SPSS system for Windows, version 22.0 is a software application for statistical analysis. It provides tools for data management, analysis, and presentation. The core function of the product is to enable users to perform a variety of statistical procedures, including regression, correlation, and hypothesis testing.
Automatically generated - may contain errors
Lab products found in correlation
8 protocols using spss system for windows version 22
1
Examining Self-Relevance Across Conditions
2
Quantitative and Qualitative Evaluation of Intervention
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Descriptive statistics for outcome measures will be presented. A paired t-test will be used to investigate statistical significant differences in the effects (primary and secondary outcomes) between intervention and control groups. Statistical significance will be defined as p ≤ 0.05. Statistical analyses will be performed using the SPSS system for Windows, version 22.0. A detailed analysis plan will be developed in collaboration with a statistical expert. This plan will also address the analysis of potential effect modifiers and confounders, and further exploratory analyses.
Qualitative data will be subjected to a thematic analysis [55 , 56 (link)]. Themes recurring from the data (interview and focus group transcripts) will be categorised and coded. The research team will meet on a regular basis and discuss emerging themes throughout the research process. Preliminary findings will be discussed with the members of the advisory group.
The quantitative outcomes will be related to, and integrated with the qualitative findings to determine correspondence and discrepancies in perceived and measured effects. The advisory group will be actively involved in discussing findings and final conclusions.
Qualitative data will be subjected to a thematic analysis [55 , 56 (link)]. Themes recurring from the data (interview and focus group transcripts) will be categorised and coded. The research team will meet on a regular basis and discuss emerging themes throughout the research process. Preliminary findings will be discussed with the members of the advisory group.
The quantitative outcomes will be related to, and integrated with the qualitative findings to determine correspondence and discrepancies in perceived and measured effects. The advisory group will be actively involved in discussing findings and final conclusions.
3
Longitudinal Analysis of Patient Outcomes
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Patient characteristics will be reported as means with standard deviations, medians with interquartile ranges or proportions when appropriate. For the analyses, the linear mixed regression model will be used. Regression analyses will be adjusted for possible confounders such as gender, age and dialysis vintage. If patients discontinue their participation of the study for any reason, all available data collected until the moment of discontinuation will be included in the analyses. Statistical significance will be defined as p ≤ 0.05 and will be performed using the SPSS-system for Windows, version 22.0 (SPSS, Chicago, IL, USA).
4
Recognition Performance Analysis
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Recognition performance was analyzed by a two-way Condition (3) × Time (3) analysis of variance (ANOVA). To control for the Type 1 error associated with violation of the sphericity assumption, degrees of freedom greater than one were reduced by the Huynh-Feldt ε correction. Paired t-tests were applied as post hoc analyses. All analyses were conducted with SPSS® system for Windows, version 22.0.
5
Circulating TSP-2 and Cardiac Biomarkers
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Statistical analysis was carried out in the SPSS system for Windows, Version 22 (SPSS Inc, Chicago, IL, USA). The data were presented as mean±SEM or 95% confidence interval (CI); median and interquartile range. Normal distribution was checked by means of Shapiro–Wilk test and Kolmogorov-Smirnov test. Two-tailed Student t-test or Shapiro–Wilk U-test were used for comparison of the main parameters of patients′ groups. Chi square test (χ2) and Fisher F exact test were used to compare categorical variables between groups. The circulating TSP-2 and NT-pro-BNP levels in the blood were not in normal distribution, while distribution of TC and cholesterol fractions had a normal character and was not subjected to any mathematical transformation. The factors, which could be associated potentially with circulating TSP-2, were determined by logistic regression analysis. Receiver operation curve (ROC) analysis was carried out to identify the optimal cut-off points of the TSP-2 concentration with predicted value. Net reclassification improvement (NRI) was used for comparison of AUC curves. Odds ratio (OR) and 95%CI were calculated for all the independent predictors of survival of the patients. A calculated difference of P<0.05 was considered significant.
6
Predictors of Decreased CD62E+ Ratio
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Statistical analysis of the results obtained was performed in SPSS system for Windows, version 22 (SPSS Inc, Chicago, Illinois, USA). The data were presented as mean (M) and standard deviation (±SD) as well as median (Me) and 25–75% interquartile range (IQR). To compare the main parameters of patient cohorts, two-tailed Student t test or Mann-Whitney U test were used. To compare categorical variables between groups, χ2test and Fisher exact test were used. Univariable and multivariable regression analysis was used for determining the predictors of decreased CD62E+ to CD31+/annexin V+ ratio. All sufficient predictors with p <0.2 obtained by univariable regression analysis were included in the multivariate regression model. A two-tailed probability value of <0.05 was considered as significant.
7
Statistical Analysis of Biomarkers in Patients
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Statistical analysis of the results obtained was performed in the SPSS system for Windows, Version 22 (SPSS Inc., Chicago, IL, USA). The data were presented as mean (М) and standard deviation (± SD); as well as median (Ме) and 25%–75% interquartile range (IQR). To compare the main parameters of patient cohorts, two-tailed Student t-test or Mann–Whitney U-test was used. To compare categorical variables between groups, Chi2 test (χ2) and Fisher exact test were used. Univariable and multivariable regression analysis was used for determination of predictors of decreased CD62E + to CD31 +/annexin V + ratio. All sufficient predictors with P value < 0.2 obtained by univariable regression analysis were included in the multivariate regression model. A two-tailed probability value of < 0.05 was considered as significant.
8
Statistical Analysis of Clinical Outcomes
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Statistical analysis of the results obtained was carried out in SPSS system for Windows, Version 22 (SPSS Inc., Chicago, IL, USA) and GraphPad Prism for Windows, Version 5 (GraphPad Software Inc., La Jolla, CA, USA). The data were presented as mean (М) and standard deviation (± SD) or 95% confidence interval (CI); median (Ме) and interquartile range (IQR), as well as number (n) and frequencies (%) for categorical variables. To compare the main parameters of patients' groups (subject to the type of distribution of the parameters analyzed), two-tailed Student's t-test or Mann–Whitney U-test was used. To compare categorical variables between groups, chi-squared test (χ2) and Fisher F exact test were used. The factors, which could be associated potentially with clinical outcomes, were determined by log regression analysis. Reclassification methods (C-statistics) were utilized for prediction performance analyses. A calculated difference of P < 0.05 was considered significant.
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