Mean (M) and standard deviation (SD) were used to describe demographic and clinical characteristics. Continuous variables of general characteristics, clinical and biological changes were described with nonparametric test (Kruskal-Wallis H test) and one-way analysis of variance (ANOVA). The data exceeding M ± 3 SD as outlier were omitted. Receiver operating characteristic (ROC) curve expressed as area under the curve (AUC) with the corresponding 95% confidence interval (CI) was set for the prediction of GDNF to PSD and for the differential function from MDD. SPSS Version 23.0 statistical software (SPSS Inc. Chicago, IL) was used to conduct all the analysis.
Spss version 23.0 statistical software
Manufactured by IBM
Sourced in United States
SPSS Version 23.0 is a statistical software product developed by IBM. It provides a comprehensive set of tools for data analysis, modeling, and reporting. The software is designed to handle a wide range of data types and offers a user-friendly interface for performing various statistical procedures.
Automatically generated - may contain errors
Lab products found in correlation
12 protocols using spss version 23.0 statistical software
1
GDNF Prediction in Psychiatric Disorders
2
Correlation between HEART Score and UA Level
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Data were analyzed using the SPSS version. 23.0 statistical software (SPSS Inc., Chicago, Illinois). Data distribution was assessed using the One‐sample Kolmogorov–Smirnov test. Descriptive data were reported as mean ± standard deviation (SD) and median (interquartile range) for normally and non‐normally distributed data, respectively. As appropriate, the comparison between quantitative variables was performed using parametric one‐way analysis of variances (ANOVA) for normally distributed data or the Kruskal–Wallis test for the non‐normally distributed variables. In addition, for comparing the qualitative variables, the Chi‐squared test was used. Spearman's correlation coefficient was used to assess the association between the HEART score and UA level. Statistical significance was defined as p < 0.05 for all the statistical tests.
3
Evaluating Biomarkers and miRNA Associations
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
SPSS Version 23.0 statistical software (SPSS Inc., Chicago, IL, USA) was used for statistical analysis. P < 0.05 was considered statistically significant. Quantitative variables are reported as mean (SD) or median (interquartile range [IQR]), as appropriate. Categorical variables are presented as numbers and percentages. Continuous variables that failed the normality test were logarithmically transformed before analysis. Non-parametric tests were used for the data that still did not have a normal distribution after this transformation. To compare differences between groups, independent samples T-test, Mann–Whitney U, or Kruskal–Wallis test were used. Spearman rank correlation coefficient (R) was used to test the association between selected miRNAs expression levels with some metabolic and inflammatory biomarkers.
4
Survival Analysis of Osimertinib Treatment
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
ORR and DCR were compared using Chi square tests and Fisher’s exact tests. OS and pOS were calculated using the Kaplan–Meier method and compared among groups using the log-rank test. Univariate and multivariate Cox regression analyses were used to explore potential prognostic factors for OS and pOS. The variables for multivariate analysis included sex, age, smoking status, Eastern Cooperative Oncology Group performance status (ECOG PS), central nervous system (CNS) metastasis, continuous osimertinib, chemotherapy post-PD, and dramatic progression. All reported p values were two-sided and p values < 0.05 were considered to indicate statistical significance. All statistical analyses were performed using SPSS® version 23.0 statistical software (SPSS, Inc., Chicago, IL, USA).
5
CVD Risk Factors and Framingham Risk Score
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The 2-tailed t-test was used for continuous data with mean ± standard deviation. The Chi-squared test was used for the relationships in categorical data with number and percentage. The Pearson correlation coefficient was used for the degree of linear relationship between two continuous variables. The Phi correlation was used for the correlation between two binary variables. The point-biserial correlation was used for the correlation between a binary variable and a continuous variable. The relationship between the FRS and CVD risk markers was examined in the simple and multiple regression analysis. The CVD risk markers, including hsCRP, WBC count, and serum UA, were centered from their means to form their interaction terms with gender in the multiple regression analysis. When an interaction term showed significance, further multiple regression analysis of the FRS for each gender was conducted separately. All outcomes of the regression analysis are presented in unstandardized coefficient (B) and standardized coefficient (β), at a 95% Confidence Interval for B. All statistical procedures were performed using SPSS (version 23.0) statistical software (SPSS Inc., Chicago, IL, USA), and a p-value less than 0.05 was considered statistically significant.
6
Statistical Analysis of Experimental Data
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
SPSS version 23.0 statistical software (SPSS Inc, Chicago, United States) was used for statistical analyses. Data are expressed as the mean ± SD, and comparisons between groups were performed by one-way ANOVA. A P value of < 0.05 was considered statistically significant.
7
Validity of Spatio-Temporal Parameters in Running
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
All values are expressed as mean ± standard deviation (SD), and in some variables 95% confidence intervals (95% CI) were also calculated. The Kolmogorov-Smirnov test was applied to ensure a normal distribution of all results. A one-way ANOVA was used to analyze the differences between both groups. The magnitude of differences or effect sizes (ES) were calculated according to Cohen's d (8) and interpreted as small (0.2 ≤ ES < 0.5), moderate (0.5 ≤ ES < 0.8) and large (ES ≥ 0.8). Intraclass correlation coefficient (ICC) was used to assess the validity of spatio-temporal parameters between both running tests (submaximal test and graded exercise test) at 11 km•h -1 and 13 km•h -1 . The ICC was greater than or equal to 0.90 for all parameters (contact and flight time, step rate and length) at both speeds. SPSS version 23.0 statistical software (SPSS, Inc., Chicago, IL) was used. Values of p < 0.05 were considered statistically significant.
Copyright ª 2016 National Strength and Conditioning Association
Copyright ª 2016 National Strength and Conditioning Association
8
Statistical Analysis of Allergic Rhinitis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Descriptive statistics were applied to determine the frequency and proportions of demographic (gender) and clinical variables (allergic diseases and aeroallergens). For continuous quantitative variables (age and anti-Staphylococcus IgE levels) after a normality assessment (measured by asymmetry and kurtosis and the Kolmogorov Smirnov test), the median and the interquartile range (IQR) were used as a measure of central tendency and dispersion. IgE levels of the AR participants vs the controls were compared using the Mann Whitney U-test. The Kruskal–Wallis test was used to compare the severity subgroups. Significant differences between groups were represented by p <0.05. P value thresholds were Bonferroni-corrected for multiple comparisons.18 (link) SPSS version 23.0 statistical software (IBM Corp. Armonk, NY) was used for data processing.
9
Statistical Analysis of Colorectal Cancer Survival
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
In this study, data for each group was expressed as mean ± standard deviation (SD). Each set of experiments were carried out at least in triplicate. Statistical analyses were performed with SPSS version 23.0 statistical software (IBM, Chicago, IL, USA). The differences between two groups were analyzed using the Student’s t-test, and the chi-square test was used to analyze the categorical variables. The Kaplan-Meier method was used to analyze the postoperative survival rate of patients with CRC, and the differences in survival rates were evaluated using the Log rank test. Univariate and multivariate analysis were performed using the Cox proportional hazard regression model to assess the significance of the relevant covariates. A P-value <0.05 was considered statistically significant.
10
Surgical Outcomes for Osteonecrosis of the Femoral Head
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The SPSS version 23.0 statistical software was used for all data statistics (IBM Cooperation, USA). For intragroup analysis, a two-sided paired t test was used to analyze the changes in normally distributed HHS and VAS score from before to after surgery, while the Mann-Whitney U test was used for intergroup analyses. The survival rate was calculated using the Kaplan-Meier method, and we defined the end point as revision to THA. Survival subgroups were divided according to ARCO stage and etiology. We calculated hazard ratios (HRs) and 95% confidence interval (CI) using Cox hazard proportional model to assess the HR of conversion to THA for patients undergoing autologous or allogeneic fibular grafts. Three multivariate models were used by controlling categorical covariates, including age, BMI, ARCR stage, and etiologies. Most of the abovementioned indexes were presented as mean ± standard deviation (SD) with or without range values. Patient satisfaction was shown as a percentage. All statistical analyses were considered significant when the p value was < 0.05.
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?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!