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Spss 21.0 statistical package

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SPSS 21.0 is a statistical software package developed by IBM. It is designed to analyze and visualize data, offering a wide range of statistical techniques and tools for data management, analysis, and reporting. The core function of SPSS 21.0 is to provide users with the ability to perform advanced statistical analyses, generate reports, and create customized visualizations of their data.

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Lab products found in correlation

Prism 6, by GraphPad (1 mentions) Spss 21, by IBM (1 mentions) Prism v6, by GraphPad (1 mentions) Sigmaplot 10, by IBM (1 mentions) Statistical software version, by MedCalc (1 mentions) Originpro 8, by OriginLab (1 mentions) Origin 9, by OriginLab (1 mentions)

51 protocols using spss 21.0 statistical package

1

Prognostic Nomogram for Survival

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All statistical analyses and random allocation were performed by SPSS 21.0 statistical package (IBM Corporation, Armonk, NY, USA) and R project version 3.3.3 (http://www.r-project.org/) for Windows. The cutoff value of LNR was determined by the receiver operating characteristic (ROC) curve. The OS was compared by Kaplan–Meier curves and analyzed using the log-rank test via GraphPad Prism 6 Software (GraphPad Software Inc., San Diego, CA, USA). The univariate and multivariate analyses and hazard ratios (HRs) were used by Cox proportional hazards regression model to find its independent prognostic risks, and P < 0.05 was considered as statistically significant difference.
A novel prognostic nomogram based on LNR for OS was formulated by the rms package in R project (Bell Laboratories, Murray Hill, NJ, USA). Its predictive performance was measured by concordance index (C-index), calibration curve, and decision curve analysis (DCA) as previously described.6 (link) The prognostic prediction was more precise with larger C-index, superior consistency, and wider threshold probability or net benefit. Bootstraps with 1,200 resample in primary cohort or 600 resample in validation cohort were used for such activities. The cutoff value of formulated nomogram staging system was determined by X-tile software. (Yale University, New Haven, CT, USA)
Pu N., Li J., Xu Y., Lee W., Fang Y., Han X., Zhao G., Zhang L., Nuerxiati A., Yin H., Wu W, & Lou W. (2018). Comparison of prognostic prediction between nomogram based on lymph node ratio and AJCC 8th staging system for patients with resected pancreatic head carcinoma: a SEER analysis. Cancer Management and Research, 10, 227-238.
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2

Symptom Scoring and Statistical Analysis

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Data are expressed as mean (standard deviation or standard error), geometric mean (95% CI), or percentage. Comparisons among 3 or more groups were performed using the chi-square test in categorical variables or analysis of variance (ANOVA) followed by Bonferroni correction in continuous variables. Two-way repeated measures ANOVA was performed to determine whether the symptom scores were different within a group and between groups at baseline, and at first and second follow-up time points. A post-hoc Tukey’s test was used to identify the group that showed a difference when ANOVA showed a significant interaction. Variables with P < 0.05 in univariate analyses or clinical importance were subjected to multivariate analyses. All statistical tests were 2-tailed. P < 0.05 was considered statistically significant. Statistical analysis was performed using the SPSS 21.0 statistical package (version 21.0, IBM, Armonk, NY, USA).
Lee J.Y., Kim N., Park J.H., Yu J.E., Song Y.J., Yoon J.W, & Lee D.H. (2022). Sex and Gender Differences in Overlap Syndrome of Functional Gastrointestinal Disorder and Effect of Genetic Polymorphisms in South Korea: A Long-term Follow-up Study. Journal of Neurogastroenterology and Motility, 28(1), 145-158.
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3

Statistical Analysis of Research Data

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SPSS 21.0 statistical package (provided by IBM, USA) was used for data entry and processing. The data accorded with normal distribution were expressed as mean ± standard deviation (x ± s), or as median (four quantile range), that is, M (P25, P75). When the data accorded with normal distribution and homogeneity of variance, one-way analysis of variance (ANOVA) and LSD-t test were used for the intergroup comparisons, or nonparametric test was used. Pearson’s correlation analysis was used for linear correlation analysis. For normality and homogeneity of variance tests, a statistical difference was defined as P < 0.1, but for other tests a statistical difference was defined as P < 0.05 and a statistically significant difference was defined as P < 0.01.
Wang Y., Zhou B., Fang S., Zhu S., Xu T., Dilikumaer M, & Li G. (2022). Dynorphin participates in interaction between depression and non-erosive reflux disease. Esophagus, 20(1), 158-169.
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4

Association of Radiologic and Histopathologic Features

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All data were expressed as the median or mean ± standard deviation with the range shown in parentheses. Univariate analyses were conducted to examine the association between radiologic and histopathologic features. Differences between groups were assessed by the Fisher's exact test and Student's t-test using the SPSS 21.0 statistical package SPSS 21.0 statistical package (IBM, Armonk, NY, USA). Significance was determined using P < 0.05, and trend-level effects were defined as P = 0.05–0.10. All P values were presented with an odds ratio (OR). OR was presented with 95% confidence interval (CI). When OR could not be calculated, risk ratio (RR) was calculated. All tests were two tailed.
Asiltürk M., Abdallah A, & Sofuoglu E.Ö. (2020). Radiologic–Histopathologic Correlation of Adult Spinal Tumors: A Retrospective Study. Asian Journal of Neurosurgery, 15(2), 354-362.
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5

Statistical Analysis of Research Data

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All the statistical analysis was performed using the SPSS 21.0 statistical package (IBM Corp., New York, USA). Descriptive data were presented in frequencies and percentage with mean and standard deviations. Bivariate analysis was performed and variables with P < 0.2 were included in the multivariate logistic regression analysis. Odds ratio (OR) with 95% confidence interval was also computed for each variable for the corresponding P value. The value of P < 0.05 was considered as statistically significant.
Abdela O.A., Bhagavathula A.S., Getachew H, & Kelifa Y. (2016). Risk factors for developing drug-related problems in patients with cardiovascular diseases attending Gondar University Hospital, Ethiopia. Journal of Pharmacy & Bioallied Sciences, 8(4), 289-295.
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6

Anxiety Symptoms in MRKH Syndrome

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The differences with respect to the results of GAD-7 between the MRKH patients and healthy women were analyzed based on one-way analysis of covariance, with the demographic variables of age, educational level and family income as covariates. Then, patients were divided into anxiety and non-anxiety groups based on the GAD-7 score. Univariable analysis was used to assess variables associated with the outcomes. Factors with a P < 0.1 in the univariable analysis were then used in the stepwise multiple logistic regression analysis to identify the potential risk factors for anxiety symptoms in patients with MRKH syndrome. The descriptive statistics are presented as the mean values ± standard deviations (SD) or medians and interquartile ranges for continuous variables and frequencies for categorical variables. Pearson χ2 and Fisher's exact tests were used to analyze categorical variables, while independent samples t tests or Mann-Whitney U tests were performed to analyze continuous variables. P < 0.05 was considered statistically significant. For pairwise comparisons, the Bonferroni-adjusted P value was calculated. All statistical analyses were performed with the IBM® SPSS® 21.0 statistical package (SPSS Inc., Chicago, IL).
Song S., Chen N., Duan Y.P., Kang J., Deng S., Pan H.X, & Zhu L. (2020). Anxiety symptoms in patients with Mayer-Rokitansky-Küster-Hauser syndrome: a cross-sectional study. Chinese Medical Journal, 133(4), 388-394.
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7

Statistical Analyses of Experimental Data

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Statistical analyses performed by IBM® SPSS 21.0 statistical package (Chicago, IL, USA). The data were tested for normality (Shapiro-Wilk’s test) and subjected to the test of homogeneity (Levene’s test). The data presented as mean ± standard deviation (SD) and the differences compared by analysis of variance (one-way ANOVA). A posthoc test (Turkey’s test) analysis performed for any significant differences found between the groups (p-value < 0.05).
Kok J.M, & Wong C.L. (2022). In vitro Properties of Methanol Extract and Sodium Alginate of Sargassum polycystum C. Agardh Brown Seaweed Collected from Malaysia. Tropical Life Sciences Research, 33(1), 55-76.
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8

Survival Analysis of Patient Outcomes

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Means and medians were calculated to summarize continuous variables, and results were compared using Student's t-test or the Kruskal-Wallis test as the non-parametric test, when normal distributional assumptions were questionable. Categorical variables are reported as numbers and percentages. Differences between groups were assessed using chi-squared or Fisher's exact tests, when needed.
Patient and graft survival analysis was conducted according to the Kaplan-Meier product-limit estimates, and patient subgroups were compared using a two-sided log-rank test. All analyses were performed using the SPSS 21.0 statistical package (IBM, USA).
Costa C.M., Porta A., Miura I.K., Porta G., Fonseca E.A., Pugliese R., Kondo M., Chapchap P., Sindhi R., Feier F.H, & Seda J N.e.t.o. (2023). In vitro fertilization: an unexpected finding in a cohort of patients with biliary atresia. Brazilian Journal of Medical and Biological Research, 56, e12671.
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9

Predictors of Severe COVID-19 Outcomes

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Categorical variables were presented in numbers and percentages, and continuous variables were presented as means and standard deviations (SD), or as medians and interquartile ranges (IQR), as appropriate. Results were compared using t-tests or appropriate non-parametric tests when distributional assumptions were in doubt, and differences between groups were assessed using chi-square or Fisher’s exact tests, when needed. The Cox regression method was used to determine independent predictors associated with the outcomes categorized as moderate and severe COVID-19 disease. The variables that presented a p-value ≤ 0.10 in the univariate analysis, and those with clinical relevance, were included in the multivariate model. Significant differences were considered at a p < 0.05. All analyses were performed using the SPSS 21.0 statistical package (IBM Inc., Chicago, IL, USA)
Freitas A.F., Pugliese R.P., Feier F., Miura I.K., Danesi V.L., Oliveira E.N., Hirschfeld A.P., Borges C.B., Lobato J.V., Porta G., Seda-Neto J, & Fonseca E.A. (2022). Impact of COVID-19 Infection on Children and Adolescents after Liver Transplantation in a Latin American Reference Center. Microorganisms, 10(5), 1030.
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10

Genetic Associations in Lung Cancer

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Statistical analyses were performed with SPSS 21.0 statistical package (SPSS, Chicago, IL, United States). The allele frequencies in the cases and controls were tested for departure from the Hardy–Weinberg equilibrium (HWE). HaploReg v4.1 (https://pubsbroadinstituteorg/mammals/haploreg/haploregphp) was used to predict the potential functions of the SNPs. Differences in the demographic variables and allele frequencies between the cases and controls were evaluated using chi-square tests and Welch’s t-tests. Associations between the genotypes and lung cancer risk were evaluated by unconditional logistic regression analysis and expressed by odds ratios (ORs) and 95% confidence intervals (CIs) using SNPstats (https://www.snpstats.net/start.htm). The interaction between SNPs was analyzed by using multifactor dimensionality reduction (MDR) software. The statistical significance was established when p < 0.05.
Liu Y., Liang X., Zhang H., Dong J., Zhang Y., Wang J., Li C., Xin X, & Li Y. (2022). ER Stress–Related Genes EIF2AK3, HSPA5, and DDIT3 Polymorphisms are Associated With Risk of Lung Cancer. Frontiers in Genetics, 13, 938787.
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