Continuous variables were described as medians with range in brackets. The Mann-Whitney U test or the t-test, where appropriate, was used to analyze parametric variables. Pearson’s chi-squared test or Fisher’s exact test, where appropriate, was used to analyze categorical variables. Youden’s index was used to estimate the optimal preoperative level of bilirubin. The receiver operating characteristic curve was used to test the sensitivity and specificity of the value, and logistic regression was used to show how the bilirubin level would affect 90-day mortality. The Kaplan-Meier method was used to calculate survival. Overall survival was calculated from the day after operation to the day of last follow-up visit or death. Disease-free survival was calculated from the day of discharge to the day of disease recurrence. The log-rank test was used for comparison of survival between groups. Univariate and multivariate analyses by logistic regression were performed to look for risk factors for 90-day mortality. P values < 0.05 denoted statistical significance. The computer software Statistical Product and Service Solutions, version 20 (IBM Corporation, Armonk, NY, USA), was used for all statistical analyses.
Statistical product and service solutions version 20
Manufactured by IBM
Sourced in United States
Statistical Product and Service Solutions, version 20, is a software package designed for statistical analysis and data management. It provides a comprehensive suite of tools for data exploration, model building, and reporting. The product is intended to assist organizations in making data-driven decisions.
Automatically generated - may contain errors
Lab products found in correlation
6 protocols using statistical product and service solutions version 20
1
Preoperative Bilirubin Predicts Mortality
2
Analyzing Coronary Artery Bifurcation
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The CE-3D-WATSc findings and intraoperative observations were compared and analyzed. Statistical analyses were performed using Statistical Product and Service Solutions version 20 (IBM Corporation, Chicago, USA). We used the Pearson chi-square test to analyse the bifurcation of the CPDAs and the maturity of the PPDAs. P < 0.05 was considered statistically significant.
3
Survival Analysis of Surgical Margins
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Continuous variables were described as median with range included in bracket. Categorical variables were analyzed using Chi-squared test or Fisher exact test where appropriate. Parametric variables were analyzed using Mann–Whitney U test or t test where appropriate. Disease-free and overall survivals were calculated from the day of discharge to the day of disease recurrence or census, that is, death or last follow up, using Kaplan–Meier method. Survivals between groups were compared using Log-rank test. Variables associated with survival with P < 0.1 in univariate analysis were put into multivariate analysis in order to identify the independent factors. P value of <0.05 was considered statistically significant. Discriminant analyses were performed to identify cut-off values of margin width that affect survival. Statistical Product and Service Solutions version 20 was used for all statistical analysis.
4
Survival Outcomes After Propensity-Matched Analysis
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Categorical and continuous variables were compared with the chi-square test and Student’s t-test, respectively. Propensity score matching was performed to decrease confounding effects due to nonrandomized assignment. First, a propensity score for each patient was calculated by logistic regression using the variables of age, sex, clinical stage, tumor location, differentiation grade, tumor length, and comorbidity score. Then, a 1:1 matched study group was created using a greedy matching algorithm. After matching, 562 well-balanced pairs of patients in groups A and B, respectively, were identified for outcome comparison. Univariable and multivariable survival analyses were analyzed using the Cox proportional hazards regression model. Survival curves were plotted using the Kaplan–Meier method and compared with the log-rank test. The overall survival was calculated as the period between the date of initial treatment and the date of death. Patients who survived to the end of the follow-up period (December 31, 2015) were censored. All statistical calculations were performed with Statistical Analysis System (version 9.3; SAS Institute, Inc., Cary, NC) and Statistical Product and Service Solutions (version 20; SPSS Inc., Chicago, IL). A p value < 0.05 was considered statistically significant.
5
Statistical Analysis of Experimental Data
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Data were analyzed using Statistical Product and Service Solutions version 20.0 (IBM Corp., Armonk, NY, USA). The differences between two groups were compared with Student's t-tests or Pearson's χ2-tests. One-way analysis of variance was used for comparisons of multiple groups. P value of <0.05 was considered as a statistically significant difference.
6
Histomorphometric Analysis of Rat Tissues
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For histomorphometric parameters, the data retrieved from each sacrificed rat was calculated as the mean of the six high-power photomicrographs. Data were analyzed using Statistical Product and Service Solutions, version 20.0 (IBM Corp., Armonk, NY, USA). For normally distributed quantitative variables, one-way ANOVA was applied, followed by post-hoc Tukey test for pairwise comparison. For non-normally distributed quantitative variables, the Kruskal–Wallis test was applied, followed by a post-hoc Dunn test for multiple comparisons. Data are expressed as means ± standard deviations, with P-values < 0.05 considered statistically significant.
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