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Pasw 19

Manufactured by IBM
Sourced in United States

PASW 19.0 is a statistical software package developed by IBM. It provides a range of analytical tools and capabilities for data management, analysis, and visualization. The core function of PASW 19.0 is to enable users to perform statistical analysis, model building, and reporting on various types of data.

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Lab products found in correlation

9 protocols using pasw 19

1

Comparing MRI and Histology Methods

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Statistical analyses were performed with PASW 19 (formerly SPSS, IBM, New York, NY, USA). The one-sample Kolmogorov-Smirnov test and Q-Q plots were used to assess normality of values. A Bland and Altman plot (or difference plot) [15 (link)] was used to compare the two methods (MRI and histology) (Figure 2). In this plot, the differences between the two methods were plotted against the averages of the two methods. Horizontal lines were drawn at the mean difference and the limits of agreement (mean difference ±1.96 times standard deviation). The Pearson correlation was used to assess the linear relationship between the two methods. Statistical differences between groups were tested by Student's paired t-tests. A P value < 0.05 was considered significant. Results were expressed as mean and standard deviation (SD).
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2

Statistical Analysis of Ocular Parameters

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Statistical analyses were performed using PASW 19 (SPSS Inc., Chicago, IL, USA). Normally distributed data are presented as mean ± standard deviation; not normally distributed data are presented as median and range (minimum to maximum). Data distribution was assessed using the Shapiro– Wilk and Kolmogorov–Smirnov tests. Differences in variances were assessed using Levene’s test. Depending on data distribution, tests for not normally or normally distributed data were used taking into account possible pairing of samples and inequality of variances. Differences in proportions were assessed using Fisher’s exact test. Results are expressed as odds ratio and 95% confidence interval. IOP was classified as increased if ≥21 mmHg with or without glaucoma medication. Hypotony was classified as IOP ≤5 mmHg.20 (link),21 (link) Corneal haze was graded as “present” or “not present”. Statistical significance was defined as P<0.05.
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3

Statistical Analysis of Experimental Data

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Data are shown as mean ± SD, unless otherwise specified. GraphPad Prism7 and PASW19 statistical package (SPSS) was used for statistical analyses. Significance was considered for p < 0.05. After verifying normal distributions using Kolmogorov–Smirnov tests, one-way ANOVA followed by a Tukey’s post hoc test or Kruskal–Wallis followed by Dunn’s post hoc test were performed. For correlation analyses, Spearmans’ ρ tests were performed.
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4

Acute Renal Injury Risk Factors

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Descriptive population characterization was performed using absolute and relative frequencies in variables with categorical scale level. For variables with continuous variables or values with ordinal scale, means and standard deviation or median and quartiles were compared. Analyses of statistical significance were performed with two-sided alpha <5%. Statistical significance tests were performed with Chi2-tests, Student’s t-test or Mann-Whitney U-test depending on the scale level and distribution of normality. Logistic regression analyses were added to obtain odds ratios (ORs) for covariables and also to define most relevant factors for the dependent variable (occurrence of acute renal injury) using backward selection regression model. To identify a suggestive cut off for SOP adherence rate, primary and secondary end points of the study were evaluated stepwise using different cut offs of SOP adherence. The corresponding LAG and HAG populations for each adherence rate were used to analyse the related significance levels for occurrence of AKI, length of ICU stay, and duration on mechanical ventilation. All analyses were performed using PASW 19.0 (SPSS Inc. 1998-2010, Chicago, Illinois 60606, USA).
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5

Retinal Vascular Measures and Gestational Diabetes

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Comparisons of characteristics between GDM and non-GDM participants were analyzed either by Student’s t-test or χ2-test. Based on the normal distribution of retinal vascular measures and fasting and 2-h glucose levels, these variables were analyzed as continuous variables. GDM status was analyzed as a binary variable (present/absent).
Multiple linear regression models were constructed to assess the associations between GDM (exposure) and the maternal retinal vascular outcomes mentioned above. Two models were applied in all analyses. Model 1 estimated associations after adjusted for socio-demographic factors, including age, ethnicity, and maternal highest education; Model 2 additionally adjusted for GDM risk factors, including personal history of hypertension, weight gain at 26–28 weeks gestation, family history of diabetes, maternal pre-pregnancy overweight/obese status, history of past GDM, and parity. All statistical analyses were performed using PASW 19.0 (SPSS Inc, Chicago, U.S.). P values and 95% confidence intervals (CIs) were presented accordingly. Significant interactions of GDM*age, GDM*ethnicity and GDM*pre-pregnancy overweight/obese status were defined as P < 0.1.
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6

Adenoma Detection and Retroflexion Analysis

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Data were reported as mean±standard deviation for normally distributed data and median and range for skewed data. Grouped continuous data were compared using two-tailed Student’s t-test and the Mann–Whitney U-test where appropriate. Intergroup and categorical comparisons were made using the Chi-square and Fisher’s exact tests. Logistic regression analysis was utilized to evaluate predictors of adenoma detection and factors associated with successful retroflexion. Analysis of variance was utilized for comparison between endoscopists. A P value of <0.05 was considered statistically significant. The primary analysis was performed in an intention-to-treat manner, and data were also re-analyzed in a per protocol manner. All statistical analyses were performed using PASW 19.0 (SPSS, Chicago, IL).
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7

Statistical Analysis of Adenoma Recurrence

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The Shapiro–Wilk test was used to assess normality of data. Normally distributed data are reported as mean ± standard error of the mean, skewed data as median and range. Grouped continuous variable data were compared using two-tailed Student’s t test and Mann–Whitney U test where appropriate. Intergroup and categorical comparisons were made using the chi-square and Fisher’s exact tests. A p value of < 0.05 was required for statistical significance. Time to adenoma recurrence was estimated using Kaplan–Meier algorithms, and the log rank test was used for intergroup comparisons [27 (link)]. All statistical analyses were performed using PASW 19.0 (SPSS, Inc., Chicago, IL).
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8

Evaluating Losartan's Impact on Ventricular Volumes in Marfan Syndrome

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Data are presented as mean value ± standard deviation and categorical variables as numbers and percentages. Between-group comparisons of quantitative and categorical variables were made by Students’ t‑test and Fisher’s exact tests, respectively. Associations between characteristics were explored with regression analysis. The effect of losartan on change in ventricular volumes and function was evaluated by the two-tailed Student’s t‑tests. All analyses were per protocol analyses in order to evaluate the change in ventricular volumes between the two groups of MFS patients who continued their losartan treatment throughout the entire study, and those in whom losartan treatment was not started during the study. a two-tailed P < 0.05 was considered statistically significant. For statistical analyses, PASW 19.0 (SPSS Inc., Chicago, IL) for Windows (Microsoft, Redmond, WA) was used.
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9

Predictors of Refractive Errors and Strabismus

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Statistical analysis of the data was carried out using the Predictive Analytic Software (PASW19.0, SPSS Inc., Chicago, IL, USA). Chi-square (χ²) tests Mann-Whitney U test and Kruskal-Wallis one-way analysis of variance were applied to assess the effects of ethnicity and gender on visual acuity, refractive error, lifestyle and parental refractive status. Prevalence of refractive errors, strabismus and spectacle coverage was calculated and compared among the ethnic groups. Univariate logistic regression was used to explore the predictors of refractive errors including parental refractive status, time spent indoors, and time spent outdoors. Predictors of strabismus were explored including ethnicity, gender, myopia prevalence, hyperopic prevalence and astigmatism prevalence and refractive errors. Model terms that were statistically significant at the 0.1 level were entered into a multivariate logistic regression to evaluate their independent effects. A p-value less than 0.05 was considered as statistically significant.
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