Data analysis was performed by using the SPSS computer software (version 21). The haplotype analysis was undertaken using the SHEsis online webserver. Each tagSNP was tested in controls for confirmation with Hardy-Weinberg Expectations (HWE) by a goodness-of-fit χ2 test. Continuous variables between the CAD/MI and control groups were presented as means ± standard deviation and compared using Student's t-test. χ2 test was used for categorical variables in the differences of the demographic characteristics at the CAD/MI and control groups. Logistic regression analysis was used to analyze the association between the risk for CAD/MI and the tagSNPs with adjustment for risk factors of CAD (sex, age, smoking, drinking, diabetes, hyperlipidemia, and hypertension). P < 0.05 was considered to indicate statistical significance.
Spss computer software
Manufactured by IBM
Sourced in United States, Japan
SPSS is a statistical software package developed by IBM. It is designed to analyze and manage data, and provides a wide range of statistical techniques for data manipulation, visualization, and analysis.
Automatically generated - may contain errors
Lab products found in correlation
38 protocols using spss computer software
1
Genetic Risk Factors for Coronary Artery Disease
2
Biomarker Diagnostic Accuracy Assessment
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SPSS computer software was used for statistical analyses. Results were expressed as the mean ± SD and all statistical comparisons were made by independent t-tests with p ≤ 0.05 considered as significant. Receiver Operating Characteristic (ROC) analysis was performed as a comprehensive way to measure the accuracy of the assessed biomarkers. The area under the curve (AUC) provided a useful metric to compare different biomarkers. An AUC value close to 1 indicated an excellent diagnostic and predictive marker; however, a curve that was close to the diagonal (AUC = 0.5) had no diagnostic utility. An AUC close to 1 is always accompanied by satisfactory values of specificity and sensitivity of the biomarker. Moreover, the predictiveness diagrams of the measured parameters were drawn, in which the x-axis represents percentile rank of the biomarker, the y-axis represents the probability of identifying the disease, and the horizontal line is the prevalence of the disease as determined by the Biostat 16 computer program.
3
ANOVA Analysis of Research Data
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Data were analyzed using the SPSS computer software (SPSS Inc. V16.00, 2006, USA). Comparison between groups were made by one-way ANOVA followed by the Tukey's Kramer test. A P-value 0.05 was considered statically significant.
4
Comparing Waiting Times for POCT and Central Lab
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Median waiting times were calculated since data was positively skewed i.e. had a long right tail. Medians were presented with 95% confidence intervals (95% CI) (Figs. 2 and 3 ), and 95% CIs were also calculated. Patients discharged home were analyzed separately from patients admitted to hospital. Finally, patient groups including only POC tested patients were compared to those analyzed by central lab only. Patients having both central laboratory testing and POCT were excluded from the study in order to get better comparability between two testing options.![]()
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Median waiting times from admission to blood sampling, laboratory results ready, imaging and discharge home with 95% confidence intervals
Median waiting times from admission to blood sampling, laboratory results ready, imaging and discharge to hospital with 95% confidence intervals
5
Statistical Analysis of Continuous and Categorical Variables
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Continuous variables between groups were compared using the independent-samples t test and within groups using the paired-samples t test. Categorical variables were compared using χ2 analysis. Statistical analysis was performed using SPSS computer software (version 24; SPSS, Chicago, IL). P <.05 was set as statistical significance.
6
Postoperative Coronal Imbalance Analysis
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Comparison of each variable between the two pattern groups was performed using independent t test for continuous variables and Chi-square analysis for categorical variables. Binary logistic regression analysis was further performed to estimate odds ratio for postoperative coronal imbalance9 (link). In binary logistic regression analysis, the preoperative coronal opposition pattern was coded as “0”, and consistency pattern as “1”. The statistical analysis was performed using SPSS computer software (version 24; SPSS, Chicago, IL, USA). P < 0.05 was set as statistical significance.
7
Statistical Analysis of Biological Data
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Statistical significance was assessed using SPSS computer software (SPSS Inc., Chicago, IL, USA). Data are expressed as means ± sem of n experiments. All data were tested for normal distribution, and power calculations were performed using G*Power software (Heinrich Heine University of Düsseldorf, Düsseldorf, Germany) [20 (link)]. Statistical differences were analyzed by 1- or 2-tailed t test for 2 groups, 1-way ANOVA, followed by a Bonferroni or Dunnett’s post hoc test (depending on whether all values or each value, respectively, was compared with a control) or 2-way ANOVA, followed by Bonferroni post hoc test. In all cases a P value < 0.05 was considered significant to reject the null hypothesis, and differences were considered significant.
8
Statistical Analysis of Experimental Replicates
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Each treatment was replicated 3 times for statistical validity. SPSS computer software was used for statistical analyses (SPSS Japan Inc., Shibuya, Tokyo, Japan) and SigmaPlot 8.0 software was used for mapping. Any differences between treatments were determined using one-way analysis of variance (ANOVA), and scored as significant if (P < 0.05). The means and standard errors of the means mean ± SE are reported. For Western blotting statistical analysis, independent-samples t-test was used to determine the significance at P < 0.05 using SPSS 15.0 version for Windows software.
9
Statistical Analysis of Experimental Data
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Data are generally presented as mean ± SD or as a percentage. Comparisons were made using Student’s t-test for normally distributed variables and Mann–Whitney’s U test for non-normally distributed variables. Categorical data were assessed using Fisher’s exact probability test. A probability of less than 5% was considered to represent a statistically significant difference. Analysis was performed using the SPSS computer software (version 11.0, SPSS Inc., Chicago, Illinois, USA).
10
Postoperative Hypoparathyroidism Risk Factors
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The statistical analyses were conducted with SPSS computer software (version 21.0). Parametric continuous variables with values expressed as mean ± standard deviation. Statistical comparison between groups was calculated using the χ2 test or Student t test. A multivariate analysis was performed by using logistic regression to identify the perioperative factors, which were considered to influence on postoperative hypoparathyroidism. The results of the multivariate analysis were expressed as odds ratio, 95% confidence interval, and P value. Statistical significance was set at P < .05.
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