The interrater agreement between the two readers for the assessment of PCNSL and HHG was analyzed by using the Intraclass Correlation Coefficient (ICC) with 95% confidence intervals and by applying a one-way intraclass correlation coefficient with a random rater assumption. The comparisons of MRI parameters between the PCNSL and HGG groups were performed using an independent samples t-test to analyze the statistical differences. The sensitivity, specificity, and accuracy for the discrimination of PCNSLs and HHGs were calculated for the image parameters that showed significant statistical differences on the previous t-test, and the corresponding optimal cut-off values were determined by a receiver operator characteristics (ROC) analysis. Statistical analyses were performed using the Statistical Package for the Social Sciences (SPSS, Version 19.0; Chicago, Illinois, USA). The alpha level of all tests was set at P < 0.05.
Statistical package for the social sciences spss version 19
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SPSS version 19.0 is a statistical software package developed by IBM. It is designed for the analysis of data in the social sciences. The software provides a wide range of statistical and analytical tools for data management, analysis, and visualization.
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21 protocols using statistical package for the social sciences spss version 19
1
Differentiating PCNSL and HGG on MRI
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Maternal Health Literacy and Social Support
3
Predictors of Surgical Failure in Glaucoma Valve Implantation
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Preoperative and postoperative IOP measurements and number of medications were compared using the Wilcoxon signed rank test. Cumulative survival rates were calculated using Kaplan-Meier survival analysis. Cox proportional Hazards regression analysis with forward stepwise elimination was used to assess potential predictors for surgical failure. We included variables previously listed as risk factors according to the litera-ture.5 (link)6 (link) The following variables were studied: Race, eye (right or left), glaucoma diagnosis, gender, preoperative IOP, previous glaucoma surgeries, preoperative glaucoma medications, preoperative use of oral acetazolamide, interval between last surgery and implant, type of implant, quadrant of valve implantation, and occurrence of hypertensive phase in the postoperative period. Data analyses were performed using Statistical Package for the Social Sciences (SPSS) version 19.0 (Inc., Chicago, IL). All reported probability values are two-tailed, and p < 0.05 was considered statistically significant.
4
Determinants of Dental Fluorosis: A Study
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The chi‐square test was used to analyze response distribution and compare differences between the groups. The univariate analysis showed that gender, family situation, parental employment, and self‐perceived oral health were significantly associated with DF. To estimate associations between DF and exposure to violence, a logistic regression analysis was used. Results are presented as adjusted odds ratios (ORs) with 95% confidence intervals (CIs); p < .05 was considered significant. We used the fitted model to estimate the impact of each type of violence and calculated the predicted cumulative odds based on the logistic regression analysis. Data were analyzed using the Statistical Package for the Social Sciences (SPSS, version 19.0; SPSS Inc) and STATA, version 14.0 (StataCorp LP).
5
Evaluating Prognostic Factors in Cancer Immunotherapy
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Patient characteristics were summarized using descriptive statistics. Response categories were assessed according to RECIST v1.1. Each nominal variable was compared using Fisher's exact test or the χ2 test. PFS was defined as the time from starting CPI treatment to the documentation of disease progression or death. PFS was estimated using the Kaplan–Meier method combined with log-rank analysis. Two-sided null hypotheses of no difference were rejected if P values were <0.05 or if the 95% confidence interval (CI) of risk point estimates was excluded. Cox proportional hazards regression modeling was employed in univariate analysis to identify significant and independent prognostic factors for various clinical parameters and molecular aberrations for survival. The analyses to evaluate the associations between genetic alterations and responses to CPIs were performed using R language 3.5 (Foundation for Statistical Computing, Vienna, Austria), while the other analyses were carried out using the Statistical Package for the Social Sciences (SPSS), version 19.0 (SPSS Inc., Chicago, IL).
6
Biomechanical Analysis of Manipulation
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The motion amplitudes, average velocities and accelerations were calculated from the original data derived from Simi Motion. Based on the data of the TR coordinate curve of the Y axis, the average time course for lifting (T1) and thrusting (T2), average coordinate value of the crests and troughs, dispersion (standard deviation) of T1, T2, as well as the crests and troughs were also calculated to compare the stability of the manipulation. All outcomes were compared between the two groups with t-tests, Χ2 tests and/or rank-sum tests using the Statistical Package for the Social Sciences (SPSS) version 19.0 (SPSS Inc, Chicago, IL, USA).
7
Fractal Analysis of Heart Rate Variability
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Normal distribution of data was examined by the Shapiro–Wilk test (appropriate for a small sample size of up to 50 subjects). We used natural logarithm of the spectral powers to obtain their normalized values. The K-means cluster analysis with Euclidean distance measure was performed for continuous variable – the ratio of the scaling exponents α1/α2. One-way ANOVA was applied to find significant difference in mean values of each variable or parameter: (1) between control and HF group; (2) between four clusters in HF group with Bonferroni post hoc test; and (3) between control and each cluster of HF with Bonferroni post hoc test. Multiple regression analysis was applied to find which variables and parameters predict the ratio of the scaling exponents in HF patients. Statistical analyses were performed using IBM Statistical Package for the Social Sciences (SPSS) version 19.0. Data are presented as mean ± standard errors. P < 0.05 was used as statistically significant.
8
Emotion Recognition Abilities in Psychopaths
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Task performance was assessed with an analysis of covariance (ANCOVA) with group as the between-subject factor (psychopaths, controls) and emotion category as the within-subject variable (happy, surprised, disgusted, angry, sad, fearful). Likewise, an independent ANCOVA was performed to assess potential differences in the number of errors to identify each emotion. These analyses were adjusted for potential confounding factors; namely: age, IQ, drug and alcohol abuse, viral infection (HIV, hepatitis B and C) and psychotropic medication, which were used as covariates. Within each emotion category, between-group differences were assessed post-hoc with independent sample t-tests. Cohen effect size was also calculated for each emotion category [42 ]. Likewise, pair-wise comparisons between each emotion category were performed with paired sample t-tests. Statistical Package for the Social Sciences (SPSS) version 19.0 software (IBM, Chicago; 2010) was used in all the analyses.
9
Cardiac Autonomic Function in Chronic Spontaneous Urticaria
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Statistical software Statistical Package for the Social Sciences (SPSS) version 19.0 (Armonk, NY: IBM Corp.) was used for statistical analysis. All the data are expressed as mean with standard deviation or median with the inter-quartile range depending on the distribution of data. The normality of data was tested by the Kolmogorov–Smirnov test. The comparison of cardiac sympathovagal indices, serum levels of cortisol and inflammatory markers, PSS and PSQI scores between patients with CSU (test group) and normal subjects (control group) was carried out using the independent-samples Student's t-test or Mann–Whitney U-test depending on the distribution of the data. The correlation of cardiac SVB (LF/HF) with serum cortisol and inflammatory markers, PSS scores and PSQI scores of patients with CSU was assessed using Spearman's rank correlation coefficient. All statistical analysis was carried out at 5% level of significance, and P < 0.05 was considered significant.
10
Sleep Characteristics and Hypertension
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IBM Statistical Package for the Social Sciences (SPSS), version 19.0 (IBM Corp., Armonk, NY, USA) was used for the statistical analysis of the data. Mean values (± standard deviation, SD) and absolute frequencies and percentages (%) were used to express continuous and categorical variables, respectively.
In particular, mean time of sleep characteristics (i.e. bedtime, rise time, time in bed and sleep duration) were expressed as HH:MM. Kolmogorov-Smirnov test was used to examine the normality of continuous data. Chi-square test and Student's t test were used to evaluate the association between hypertension and participants' demographic and sleep characteristics. Multivariate stepwise logistic regression models were constructed to evaluate the independent effect of sleep duration and sleep disturbances on the prevalence of hypertension, controlling for the effect of all possible confounders. To express all the above associations, odds ratios (OR) with their 95% confidence intervals (CI) were estimated. All tests were two tailed and statistical significance was considered for p values <0.05.
In particular, mean time of sleep characteristics (i.e. bedtime, rise time, time in bed and sleep duration) were expressed as HH:MM. Kolmogorov-Smirnov test was used to examine the normality of continuous data. Chi-square test and Student's t test were used to evaluate the association between hypertension and participants' demographic and sleep characteristics. Multivariate stepwise logistic regression models were constructed to evaluate the independent effect of sleep duration and sleep disturbances on the prevalence of hypertension, controlling for the effect of all possible confounders. To express all the above associations, odds ratios (OR) with their 95% confidence intervals (CI) were estimated. All tests were two tailed and statistical significance was considered for p values <0.05.
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