Continuous variables were described as median (interquartile range [IQR]). OS was defined as the period from the day of operation to death or the last follow-up (December 2016). The OS of all patients in this study was analyzed and recorded. Survival distribution was estimated by the Kaplan–Meier method. The log-rank test was used to evaluate the equality of survivor functions across groups. The Cox proportional hazards model was used with a 95% CI for the univariate and multivariate analyses. The chi-square test was used to compare baseline characteristics of patients between the LDH groups. All statistical tests were two-sided. Statistical significance was defined as P < 0.05. All analyses were performed using SPSS software for Windows (version 19; IBM SPSS, Somers, NY, USA).
Spss software for windows version 19
Manufactured by IBM
Sourced in United States
SPSS software for Windows version 19.0 is a comprehensive statistical analysis and data management software package. It provides a broad range of statistical procedures to analyze data, including descriptive statistics, bivariate statistics, prediction for numerical outcomes, and prediction for identifying groups.
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Lab products found in correlation
70 protocols using spss software for windows version 19
1
Survival Analysis of Patients
2
Factors Impacting Progression-Free Survival
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Clinicopathological characteristics between two groups (patients with a PFS ≥ 6 months and < 6 months) and ORR and CBR among different groups were compared using the Chi‐square test. The OS, DCB, and PFS were assessed using Kaplan‐Meier estimates and the log‐rank test. The Cox proportional hazards model with 95% confidence interval (CI) was used for the univariate analysis to verify the factors significantly related to PFS and OS. Variables with a P value less than 0.1 in the univariate analysis were included into multivariate Cox model to identify independent prognostic factors. Results were considered significant if P was less than 0.05. All analyses were performed using SPSS software for Windows (version 19; IBM SPSS, Somers, NY, USA).
3
Factors Influencing Bone Mineral Density
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Descriptive statistics were reported as percentages, means ± standard deviations, or medians (25th–75th percentile) for non-normally distributed variables. Multiple stepwise linear regression analysis was used to identify the factors associated with BMD and T-score after adjusting for age, a history of thyroid cancer, log-transformed menopausal years, GNRI, ASM/Ht2, estimated glomerular filtration rate, total calcium, log-transformed thyroid-stimulating hormone, log-transformed free T4, log-transformed T3, log-transformed parathyroid hormone, log-transformed vitamin D, log-transformed bone alkaline phosphatase (ALP), log-transformed C-terminal telopeptide, log-transformed follicle stimulating hormone, log-transformed estradiol, log-transformed cortisol, log-transformed insulin-like growth factor-1 (IGF-1), log-transformed testosterone, log-transformed thyroglobulin, log-transformed microsomal Ab and log-transformed thyroglobulin Ab. A p value < 0.05 was considered to be statistically significant. All statistical analyses were performed using SPSS software for Windows version 19.0 (SPSS Inc., Chicago, IL, USA).
4
Analyzing Immune Responses and Survival in Vaccinated Mice
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All statistical analyses were performed using SPSS software for Windows version 19.0. The differences in each variable, including antibody responses, lymphoproliferation assays and cytokine production, among all the groups were compared by one-way ANOVA. The tachyzoite burdens and survival times for vaccinated and control mice were compared using the Kaplan–Meier method. Significant differences in comparisons between groups were defined at p < 0.05.
5
Statistical Analysis of Mood Disorders in SLE
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Categorical data were summarized as counts and percentages, while continuous data were reported as the mean and standard deviation. Categorical variables were compared with the chi-square/Fisher’s exact tests, while continuous variables were compared with the Student’s t-test. Receiver-operator curves (ROC) were drawn to identify optimal levels of SLEDAI for predicting depression or anxiety. The statistical analysis was performed using SPSS software for Windows Version 19.0 (SPSS Inc., IL, USA). A p-value less than 0.05 was considered statistically significant.
6
Seasonal Variations in Indoor Air Microbiome
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Statistical analysis was performed using SPSS software for Windows, version 19.0. ANOVA variance analysis was performed on bacteria and fungi indoor air concentrations using the season, floor, and morning or afternoon as critical parameters. Pearson’s correlation coefficient was used to analyze the relationship among the selected parameters.
7
Factors Associated with Abnormal Echocardiography in CKD and DM
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All statistical analyses were performed using SPSS software for Windows version 19.0 (SPSS Inc. Chicago, USA). Data were expressed as percentages, means ± standard deviations, and medians (25th-75th percentile) for triglycerides. Between-group differences were assessed using the chi-square test for categorical variables and the independent t-test for continuous variables. Multivariate binary logistic analysis and linear regression analysis were used to identify factors associated with abnormal echocardiographic findings in the patients with CKD and DM. Covariates included in the multivariate model included age, sex, smoking history, HTN, CAD, cerebrovascular disease, systolic and diastolic BP, eGFR, proteinuria and BMI, and levels of albumin, fasting glucose, triglycerides, total cholesterol, hemoglobin, CaXP product and uric acid, as well as the use of medications including ACEIs, ARBs, β-blockers, calcium channel blockers and diuretics. A p value of less than 0.05 was considered to indicate statistical significance.
8
Intraobserver Repeatability of Novel Method
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Statistical analysis was performed using SPSS software for Windows version 19.0 (SPSS Inc., Chicago, IL, U.S.) and Microsoft Office Excel. To determine the intraobserver repeatability of this new method, mean standard deviation (mean SD), general linear model of repeated measurement, Pearson correlation analysis, and intraclass correlation coefficients (ICCs) were calculated for the three repeated measurements obtained by the same technician. The closer the ICC is to 1, the better the measurement consistency.
Bland-Altman plots[22 (link)] were created to assess agreement between three continuous measurements, and the 95% limits of agreement (LoA) were calculated as the mean ± 1.95 SD of the difference. On the Bland-Altman graphs, the difference between each pair of measurements is displayed in the Y-axis, while the mean value in the X-axis. The central line represents the mean of each pair of measurements differences and the broken lines represent the 95% LoA.
Bland-Altman plots[22 (link)] were created to assess agreement between three continuous measurements, and the 95% limits of agreement (LoA) were calculated as the mean ± 1.95 SD of the difference. On the Bland-Altman graphs, the difference between each pair of measurements is displayed in the Y-axis, while the mean value in the X-axis. The central line represents the mean of each pair of measurements differences and the broken lines represent the 95% LoA.
9
Postoperative Outcomes Analysis
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Statistical analysis was performed using SPSS software for Windows Version 19.0 (SPSS, Chicago, IL). A paired t test was used to assess the statistical significance of postoperative parameters change from the preoperative in CL, ROM, DH, VAS, JOA, NDI, and SF-36. P values of <.05 were considered statistically significant.
10
Comparative Statistical Analysis of Treatments
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All data were expressed as the mean ± standard deviation (SD). Statistical significance was evaluated using Student's t-test and two-way ANOVA in the treatments. Differences were considered to be statistically significant if P < 0.05. All calculations and statistical analyses were performed using SPSS software for Windows version 19.0 (Chicago, IL, USA).
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