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Spss statistics software version 26

Manufactured by IBM
Sourced in United States, Japan

SPSS Statistics software version 26 is a data analysis and statistical software package developed by IBM. It provides a comprehensive set of tools for data management, analysis, and reporting. The software is designed to help users collect, analyze, and interpret data effectively.

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216 protocols using spss statistics software version 26

1

Bone Volume Percentage Analysis

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Statistical analysis was conducted using the IBM SPSS Statistics software (version 26) (IBM Corporation, Armonk, NY, USA). The Shapiro-Wilk test confirmed normal distribution of the bone volume percentage data. One-way ANOVA and Bonferroni’s post-hoc tests were carried out separately for the wildtype and Crouzon models to assess whether bone volume percentage (outcome) varied significantly between treatment groups (variables). Statistical significance was set at the 0.05 probability level. Statistical interpretation was made by taking into account both the P-values and effect size (Cohen’s f). Effect size was calculated by using the formula: f=√(ƞ2/(1–ƞ2)), where ƞ2 (partial ita-squared) was obtained as a MANOVA output. The effect size (f) of 0.10 is mild, 0.25 is moderate, and 0.40 is high.
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2

Statistical Analysis of Survival Outcomes

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Statistical analysis was performed with IBM SPSS-Statistics Software Version 26 (IBM Corporation; Armonk, NY, USA). In R the package “survminer’ was used for plotting survival curves [17 ].
To analyze overall survival, Kaplan–Meier estimator was used. To adapt to the prolonged observation period, we used both Breslow and log rank test to check for statistical significance and receive a more detailed interpretation. Chi-squared tests and Fisher’s exact test were performed to compare qualitative and categorical variables, while independent samples T-test, due to the small sample size and its robustness against possibly skewed data, was used to compare continuous variables. The Kruskal–Wallis test was carried out for ordinal variables and the one-way ANOVA for continuous variables for several independent samples. Odds ratio and confidence intervall for factors, which might influence the development of CAL or therapeutical outcome, were calculated using the chi-squared test. A post hoc Bonferroni correction followed this to adjust for multiple testing. Pairwise deletion was used for missing data. Statistical significance was assumed for a p-value ≤ 0.05.
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3

Demographic Data Impact on Outcomes

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Only data from respondents who had completed all questions about demographic data were considered valid for statistical analysis. In all analyses, respondents who did not know the answer to a question were pooled with respondents who did not provide an answer to that question. The Pearson goodness of fit chi2-test was used to compare frequencies between the categorical variables. Pearson chi2-test was used to test if variables in the demographic data (section A) were independent of the outcome in questions in section B. If any variable was not independent of the outcome in any question in section B, a logistic regression analysis was done. A two-sided p-value of <0.05 was considered statistically significant. All analyses were performed using IBM SPSS statistics software version 26 (SPSS Inc., Chicago, IL, USA).
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4

Predicting Major Complications and Outcomes in Elderly PEG Patients

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Categorical data are expressed as a number and percentage (%).
Statistical analyses were performed using Student's t‐test for normally distributed continuous variables, a chi‐square test, and a Fisher's exact test for noncontinuous variables. To identify parameters influencing major complications, we examined potential factors using univariate analysis, and after determining relevant risk factors (P values <0.05), these factors were entered into a multivariate analysis using a binary logistic regression model. Odds ratios (ORs) and corresponding 95% confidence interval (CI) were generated for all variables. P values <0.05 were considered significant. To identify the parameters influencing mortality and PEG removal, Cox proportional hazard models were used for multivariate analysis using significant variables. A hazard ratio (HR) and 95% (CI) were determined. Kaplan–Meier curves were drawn and compared using the log‐rank test and log‐rank (mantel‐cox) test. Data were analyzed with IBM SPSS Statistics software, version 26.
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5

Fear Conditioning and Extinction Biomarker Analysis

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All statistical analyses were performed using SPSS Statistics software, Version 26 (IBM), and results were expressed as group means ± standard deviations. Tests for normality (Shapiro—Wilk's) were performed on each treatment group (SPS/Control) within each time point (Day 1/Day 3/Day 7/Day 17) to ensure that all data sets were normally distributed and that subsequent parametric analyses could be used. To determine differences in fear expression during CFC, two‐way repeated measures ANOVA was performed with unconditioned stimulus (US) as a within‐subjects factor and SPS‐exposure status as a between‐subjects factor. To determine differences in fear expression during Fear Extinction, two‐way repeated measure ANOVA was performed with “Day” as a within‐subjects factor and SPS‐exposure status as a between‐subjects factor. Differences between the means of the changes in gene expression (2−ΔCt values and 2–ΔCt ratios), GR protein expression, and %Freezing Time were evaluated using one‐way ANOVA (significance threshold was p < .05). Correlation between gene expression and fear behavior was evaluated using the Pearson Correlation.
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6

Statistical Analysis of Results

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The statistical analysis of the results was performed using the IBM SPSS Statistics software version 26 (IBM, Armonk, NY, USA). The non-parametric test Kruskal-Wallis for independent samples was used, considering p = 0.050 as the level of statistical significance. Statistically significant differences are given by p values < 0.050. All quantitative values are expressed as mean ± standard error of the mean (S.E.M.) for cell culture studies, and mean ± standard deviation (SD) for nanoparticle characterization studies of three replicates.
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7

Gastroschisis Feeding Protocol Outcomes

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Descriptive statistics include counts (percentages), mean ± SD, and median (25th–75th percentile) as appropriate based on distribution. Cox proportional hazards regression modeling was performed to determine the adjusted association between MOM dose as a continuous variable and time from initiation of feeding to discharge. Covariates included in the model were simple versus complex gastroschisis [19 (link)], birth gestational age [21 (link)], and year of birth to account for any changes in practice over the course of the study period. A secondary analysis was conducted to evaluate two specific cut-points for MOM dose: (1) >50% MOM versus ≤50% MOM, and (2) 100% MOM versus 0–99% MOM. Survival curves were generated using the Kaplan–Meier method. Cox proportional hazards regression modeling was performed using the same covariates to determine the adjusted association between MOM dose and the secondary outcomes of PN duration and LOS. MOM dose was evaluated as a continuous variable and as the two dichotomous MOM dose categories. All statistical analyses were performed using SPSS® Statistics software version 26 (IBM, Armonk, NY). Significance was set at α < 0.05.
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8

Statistical Analysis of FET Outcomes

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Descriptive statistics were used to calculate typical measures in patients’ demographic and clinical characteristics. Data were expressed as median with a range, and categorical data were expressed as counts and frequencies. Statistical analyses were carried out using IBM SPSS Statistics software version 26 (Statistical package for the Social Sciences Statistical Software; SPSS Inc, IBM Corporation, Armonk, NY, USA).
We analyzed the FET outcome data using R statistical software (R version 3.1.1 (2014-07-10), R Foundation for Statistical Computing, Vienna, Austria, https://www.R-project.org/ (accessed on 16 June 2022). To statistically assess significant differences in ROC curves we performed a non-parametric ROC analysis [20 (link)] We used the optimal operating point on the ROC curve using the Youden index, and uncertainty in cut-off values was modelled using a large number approach [21 (link)]. We compared patient groups using the Mann-Whitney-Wilcoxon rank-sum U test [22 (link)]. We combined TAC score, IDH mutation status, and TBRmean predictions using the logistic regression (LR) model. When used on patients grouped by IDH mutation status, we only used TAC score and TBRmean variables in the LR model. Results with a p-value below 0.05 were deemed statistically significant, and 95% confidence intervals (CI95%) were used to quantify uncertainty in statistically derived values.
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9

Placental Perfusion Analysis in FGR

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The statistical analysis was carried out using the IBM SPSS Statistics software version 26 (IBM, Armonk, NY, USA). Mean and standard deviation were used to describe quantitative variables, while in the case of qualitative variables percentages were used. Normality of the data was evaluated with the Shapiro-Wilk test. First, a bivariate was carried out, using Student’s t-test for independent samples or Mann-Whitney U-test for quantitative variables, while the Chi-square test was used for qualitative variables. Statistical significance was set at 0.05. To detect at least a 55% difference between the PI and PV between normal and FGR placentas, with a 5% alpha error and an 80% power, we needed 20 patients per study group.
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10

Data Analysis Methodology for Comparative Study

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Data that were continuous were reported as means with standard deviations, while categorical data were reported as frequencies with percentages. The Shapiro-Wilk test was used to assess the normality of the data. The Mann-Whitney U test was used to compare nonparametric continuous variables between 2 groups. Statistical significance was set at P < .05. All statistical analyses were performed using Statistical Package for the Social Sciences (SPSS) Statistics software Version 26 (IBM Corp).
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