Data were censored at the time of death or last contact. Propensity score matching was performed to reduce or eliminate confounding effects. Each ISD‐SCT case was matched with three HID‐SCT cases using the nearest neighbor‐matching method. Age and sex of patients, disease status, cytogenetic risk, sex of donor, and graft type were included in the propensity score model. Continuous variables were compared using the Mann‐Whitney U‐test. Categorical variables were compared using the χ2 and Fisher's exact tests. Survival functions were estimated using the Kaplan‐Meier method. Competing risk analysis was used to calculate the cumulative rates of GVHD, relapse, and non‐relapse mortality, and the Gray's test was used to test the differences between the HID‐SCT and ISD‐SCT groups. All reported P values were based on two‐sided hypothesis tests, and P < 0.05 was considered as having statistical significance. Data analyses were primarily conducted with the SPSS software package 22.0 (SPSS Inc., Chicago, IL, USA), and R software (version 3.3.1; http://www.r-project.org ) was used for propensity score matching [20 ], competing risk analysis [21 ] and estimating survival.
Spss software package 22
Manufactured by IBM
Sourced in United States
SPSS software package 22.0 is a data analysis and statistical software tool developed by IBM. It provides a comprehensive set of features for data management, analysis, and reporting. The software is designed to help users interpret data, identify trends, and make informed decisions.
Automatically generated - may contain errors
Lab products found in correlation
8 protocols using spss software package 22
1
Haploidentical vs. Matched Sibling Donor Transplant
2
Evaluating CD147 Expression in Breast Cancer
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All statistical analyses were performed using SPSS software package 22.0 (SPSS Inc, Chicago, IL). The other quantitative data were presented as means ± SD. For tissue array immunohistochemistry analysis, Mann–Whitney U test was used to assay the association between CD147 expression and clinicopathological variables in BC tissues and tumor adjacent tissues. A sample t-test was used to test the results of mRNA expression, and p values less than 0.05 were considered to be significant.
3
Statistical Analysis of Treatment Effects
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The SPSS software package (22.0) was used to perform the statistical analyses. The data were subjected to a one-way ANOVA, after verifying the normality and homogeneity of variances, to determine the significance of treatment and year. Duncan’s test was used for multiple comparisons. In order to find significant correlations between the results Pearson’s correlations were carried out. Furthermore, the data were also subjected to a two-way ANOVA to determine the significance of the interaction (Year X Treatment). Statistical significant differences at the 0.05, 0.01, and 0.001 level of probability were indicated by *, **, and ***, respectively.
4
Statistical Analysis of Experimental Data
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All statistical analysis was performed using SPSS software package 22.0 (IBM, SPSS, USA) and GraphPad Prism 6 (GraphPad Software, La Jolla, CA, USA). Results were expressed as mean ± SD. Differences between the different groups were estimated by Student’s t test as appropriate. p value < 0.05 was considered as statistically significant.
5
Statistical Analysis of Experimental Data
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Numerical data were shown as mean ± standard deviation (SD) from at least three independent repetitive experiments. Statistical analyses were carried out using IBM SPSS software package 22.0. Multiple groups were analyzed by one-way analysis of variance (ANOVA), followed by Bonferroni's post hoc test. Student's t test was used to analyze the differences between the two groups. The difference was considered statistically significant when P < 0.05.
6
Statistical Analysis of Experimental Data
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All statistical analyses were performed using SPSS software package 22.0 (IBM, SPSS, USA) and GraphPad Prism 6 (GraphPad Software, La Jolla, CA, USA). Results are expressed as mean ± SD. The significance of differences between the different groups was analyzed by Student's t-test as appropriate. A p < 0.05 was considered statistically significant.
7
Evaluating Behavioral Intention Factors
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Descriptive analysis was used to describe the sample profile; t-tests and one-way ANOVA were used to test H2 by examining differences between the average scores for each demographic factor. Multiple regressions were applied to test H1 for the effects of the independent variables AT, SN, and PBC on BI, and to test H3 for the effects of PBE on BI. The significant level was set at p ≤ 0.05. Confirmatory factor analysis was applied to examine the reliability and validity of each construct. The bias-corrected percentile of the bootstrapping method was used to determine whether the mediating effect was present by checking the lower and upper bounds of indirect effects. The analytic techniques were applied using the SPSS 22 software package, as well as AMOS 22.0 (IBM Corp, Armonk, NY, USA. Sourced from TriStar, Kaohsiung City, Taiwan). All significance levels were set at p ≤ 0.05.
8
Factors Influencing Perceived Smartphone Usage
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Descriptive analysis was used to describe the sample profile; t-tests and one-way ANOVA were used to examine the differences in average scores of each variable for each demographic factor. Multiple regressions were applied to test H1 for the effects of the independent variables of perceived threats, net perceived benefit, and self-efficacy on PSU intention, to test whether the most polluted area of Siaogang will have higher threat, net benefit perception, and PSU intention than other areas, and to test H3 for the moderating effects of KA on the links with PSU. The analytic techniques were applied using the SPSS 22 software package (Armonk, NY: IBM Corp. Sourced from TriStar, Kaohsiung City, Taiwan). All significance levels were set at p ≤ 0.05.
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