All statistical analyses were carried out using IBM Statistical Package for the Social Sciences (SPSS) Statistics version 22 (IBM Cooperation, New York). Univariate analysis (chi-square test) was used to study the demographic, lifestyle and clinical factors between groups. Linear mixed model and linear regression were used to evaluate the bacterial abundance longitudinally and at specific time points respectively, as well as to explore the effect of demographic factors on abundance of main bacterial groups, while adjusting for potential confounders. All statistical significance tests and confidence intervals were two-sided and set at p<0.05. Geometric means were used to represent bacterial abundance and SDI due to non-parametric data distribution.
Statistical package for the social sciences spss statistics version 22
Manufactured by IBM
Sourced in United States
SPSS Statistics version 22 is a software package used for statistical analysis. It provides a comprehensive set of tools for data management, analysis, and visualization. The core function of SPSS Statistics is to enable users to perform a variety of statistical procedures, including, but not limited to, descriptive statistics, correlations, regressions, and hypothesis testing.
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Lab products found in correlation
4 protocols using statistical package for the social sciences spss statistics version 22
1
Longitudinal Bacterial Dynamics Analysis
2
Psychological Distress in Overtime Workers
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We conducted multiple logistic regression analyses to estimate the prevalence of odds ratios (ORs) and their 95% confidence intervals (CIs) of psychological distress (defined as having a score of five or more on the K6 scale). In the analyses, we first tested the main effects of overtime work hours and RSH on psychological distress. Thereafter, we tested the interaction effect of overtime work hours and RSH on psychological distress. When we observed a significant interaction effect, we conducted stratified analyses using RSH. In a series of analyses, we first calculated the crude ORs (i.e., without any adjustment) (Model 1). Then, we adjusted for demographic characteristics (i.e., gender, age, marital status, residence status, educational background, present illness, and residence area) (Model 2) and additionally adjusted for occupational characteristics (i.e., job type, frequency of remote working, and psychosocial work characteristics) (Model 3). The level of significance was set at p < 0.05. Statistical analyses were performed using IBM Statistical Package for the Social Sciences (SPSS) Statistics version 22 (SPSS Inc., Chicago, IL, USA).
3
Evaluating Survival Characteristics in Cancer
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The differences of clinical characteristics as mentioned above were evaluated using chi-square tests in GraphPad Prism 5.0 software. The univariate Cox proportional hazards regression analysis was firstly performed to elucidate the association between differential promoter methylated genes and overall survival (OS). Kaplan–Meier survival analysis and univariate/multivariate Cox proportional hazards regression analyses were sequentially carried out in IBM Statistical Package for the Social Sciences (SPSS) Statistics Version 22.0. The predicted values from the logistic regression model were employed to construct receiver operating characteristic (ROC) curves and then to calculate the area under the ROC curves (AUC) through “pROC” package of R soft. P-values <0.05 were considered statistically significant.
4
Preoperative Biomarkers and Oncological Outcomes
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Student's t-test and the Chi-squared test were used to evaluate continuous variables and dichotomous variables. Associations between fibrinogen and adverse pathological outcomes were assessed using logistic regression analysis, in which odds ratios (ORs) and the corresponding 95% confidence intervals (CIs) were calculated. Probabilities of CSS, RFS, and OS were determined using Kaplan–Meier curves. The log-rank test was used to assess the differences between groups. Multivariate Cox proportional hazard models with forward stepwise methods were used to assess the risk factors for CSS, RFS, and OS. C-index was calculated to assess the improvement in discrimination when adding preoperative laboratory factors to the base model, using the R package “survival.” Decision curve analyses were performed to show the benefit of multivariate models that contained preoperative biomarkers. A two-sided probability (P) value of <0.05 was considered statistically significant. Statistical analyses were performed using the IBM Statistical Package for the Social Sciences (SPSS) Statistics version 22.0 (IBM Corp., Armonk, NY, USA).
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