The SPSS Version 19.0 statistical package (IBM Corporation, Armonk, NY, USA) was used for all statistical calculations. Descriptive analyses were performed based on the calculation of frequencies and percentages. In the bivariate analysis, χ2 or Fisher’s exact test was used to calculate the association between exposure to each of the physical, verbal, and sexual abuses and the participants’ characteristics. Multivariate logistic regression model was used to examine the risk factors associated with exposure to physical and verbal abuse. A significant difference was assumed when p<0.05.
Spss version 19.0 statistical package
Manufactured by IBM
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SPSS version 19.0 is a statistical software package developed by IBM. It provides a comprehensive set of tools for data analysis, including data manipulation, statistical modeling, and reporting. The core function of SPSS is to assist users in conducting quantitative research and analysis across a wide range of industries and disciplines.
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Lab products found in correlation
10 protocols using spss version 19.0 statistical package
1
Statistical Analysis of Abuse Exposure
2
Mortality Predictors in Diabetic Foot Ulcers
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Quantitative data were expressed as the median and interquartile range (Q25–Q75), and qualitative data were reported as absolute values and percentages (%).
Univariate and multivariate Cox regression adjusted for independent variables in two models was used to determine which variables were associated with mortality. Multivariate analysis models were developed from the independent variables with a p < 0.05 and those of clinical interest: age, sex, years since diagnosis, type of diabetes mellitus (DM), active smoking, CKD, retinopathy, cardiovascular disease, history of amputation, and HbA1c. The variables were grouped into two models, 1 and 2. Hazard ratio (HR) (95% confidence interval (95% CI)) was used as a risk measure. Kaplan—Meier function and log-rank test were used to test the equality of survivor functions between the various groups and showed some variables of clinical interest for the present study. Survival analysis was estimated 5 years after the first evaluation at the MDFT. The log-rank test was obtained from the first 5 years of the survival analysis. Reulceration was analyzed in the subjects who survived after the first episode of ulceration and who had not required a major amputation. The SPSS version 19.0 statistical package was used (IBM, Armonk, NY, USA). Values of p < 0.05 were considered statistically significant.
Univariate and multivariate Cox regression adjusted for independent variables in two models was used to determine which variables were associated with mortality. Multivariate analysis models were developed from the independent variables with a p < 0.05 and those of clinical interest: age, sex, years since diagnosis, type of diabetes mellitus (DM), active smoking, CKD, retinopathy, cardiovascular disease, history of amputation, and HbA1c. The variables were grouped into two models, 1 and 2. Hazard ratio (HR) (95% confidence interval (95% CI)) was used as a risk measure. Kaplan—Meier function and log-rank test were used to test the equality of survivor functions between the various groups and showed some variables of clinical interest for the present study. Survival analysis was estimated 5 years after the first evaluation at the MDFT. The log-rank test was obtained from the first 5 years of the survival analysis. Reulceration was analyzed in the subjects who survived after the first episode of ulceration and who had not required a major amputation. The SPSS version 19.0 statistical package was used (IBM, Armonk, NY, USA). Values of p < 0.05 were considered statistically significant.
3
Prolonged Bradycardia Risk Factors
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Data were entered in IBM SPSS version 19.0 Statistical Package. Descriptive statistics in the form of frequency and percentages were calculated for categorical variables and mean ± standard deviation were calculated for interval variables. Chi-square tests were performed to see association between prolonged bradycardia versus no prolonged bradycardia for all the categorical variables, whereas Student's t-test and Wilcoxon's rank sum test, wherever applicable, were performed for interval variables. Logistic regression with enter method, taking important variables for prolonged bradycardia has been used to see risk factors and presented with adjusted odds ratios (OR) and 95% confidence interval (CI). Two-tailed P value has been considered for statistical significant level.
4
Lung CT Analysis for Drowning Detection
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When the mask was created, the mean CT value (in Hounsfield units = HU) of the pixels in the whole lung was calculated and stored to create a lung CT dataset, which was imported into another software programme (OriginPro® , Originlab Corp., Northampton, MA, USA), where the pixel value distribution of the whole lung was expressed in graphical format. In the graph, the X-axis depicts the HU value, and the Y-axis depicts the corresponding number of pixels. Mean distribution curves for both drowning and control groups were created. This function estimated the average number of pixels of different HU values in the individual cause of death. The mean distribution curves reflected the influence of cause of death on lung CT value and weakened the effect of individual factors to some extent.
The radiological data analyses were performed by two forensic pathologists and two radiological specialists. Student’s t-test by SPSS Version 19.0 Statistical Package (IBM, Armonk, NY, USA) was used to examine the significance of the differences between the groups. P < 0.05 was considered significant.
The radiological data analyses were performed by two forensic pathologists and two radiological specialists. Student’s t-test by SPSS Version 19.0 Statistical Package (IBM, Armonk, NY, USA) was used to examine the significance of the differences between the groups. P < 0.05 was considered significant.
5
Comprehensive Patient Characteristics Analysis
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Patient demographics and clinical characteristics were summarized using the mean±standard deviation, median (interquartiles), and number (percentage) when appropriate. The comorbidity analysis results were described with numbers and percentages. Similarly, the DDI analysis results were summarized with numbers and percentages. All tests were two-sided. All analyses were performed with the SPSS version 19.0 statistical package (SPSS, Inc., Chicago, IL, USA).
6
Ocular Biomechanics and Biometry Analysis
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Statistical management and analysis were conducted using SPSS version 19.0 statistical package (SPSS, Chicago, Illinois, USA). Independent sample t-tests were used to assess if each parameter had a normal distribution. Multiple linear regression models were composed with CH and CRF as the dependent variables and CCT, SE, corneal curvature, AL, IOP as the covariates. Kruskal–Wallis test and paired t -test were used for intergroup comparisons.The results were considered statistically significant at a P value less than 0.05 for the CH and CRF.
7
Genetic Variants and Acute Heart Failure Prognosis
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Continuous variables were expressed as the mean ± standard deviation (SD) and compared by student’s t-test or one-way ANOVA for normal distribution, or expressed as median with inter-quartile range (IQR) and compared by Mann-Whitney U test or Kruskal-Wallis H test for skewed distribution. Categorical variables and frequency of events were reported as numbers (percentages) and compared by chi-square test. In the correlation analysis, after the logarithmic transformation of the data that skewed distribution, the Pearson method is used for the analysis. The online SHESIS software (http://analysis.bio-x.cn/myAnalysis.php ) was used to analyze the HWE, genotype, allele frequency distribution, linkage disequilibrium and SNP haplotypes (30 (link), 31 (link)). Kaplan-Meier and multi-variable COX analysis was used to analyze the prognosis of AHF patients under different genetic models of SNPs. Correlation analysis between haplotype and AHF prognosis was performed, the P value was subjected to FDR (False Discovery Rate) correction and Bonferroni correction.
All statistical analyses were two-sided and the significance level was set to P < 0.05. When D’ >0.8 and r2 >0.33, linkage disequilibrium (LD) was considered between sites. SPSS version 19.0 statistical package (SPSS, Chicago, IL, USA) and Microsoft Excel were used for all statistical analyses.
All statistical analyses were two-sided and the significance level was set to P < 0.05. When D’ >0.8 and r2 >0.33, linkage disequilibrium (LD) was considered between sites. SPSS version 19.0 statistical package (SPSS, Chicago, IL, USA) and Microsoft Excel were used for all statistical analyses.
8
Evaluation of Statistical Analyses
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Numerical data are presented as the mean ±standard deviation (SD). Data were analysed using SPSS version 19.0 statistical package (SPSS, Chicago, IL). Data were evaluated by linear regression analysis and correlations were assessed using Pearson correlation coefficients. For non-parametric data, the differences between two groups were analysed using the Mann–Whitney U-test. P-values <0.05 were considered statistically significant.
9
Statistical Methods for Robust Data Analysis
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All statistical analyses were performed using the SPSS version 19.0 statistical package (SPSS Inc., Chicago, IL). Continuous variables were expressed as mean ± standard deviation, and were compared by either the unpaired Student’s t-test or one-way analysis of variance (One-way ANOVA). Discrete variables were expressed as counts and percentages, and the proportions were compared by using the Chi-square test. We used binary logistic regression analysis to estimate the odds ratios (OR) and 95% confidence intervals (CI), adjusting for factors that were considered to potentially influence the results. All statistical analyses were two-tailed, and P-values < 0.05 were taken as statistically significant.
10
Biostatistical Analysis of Experimental Data
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Numerical data are presented as the mean±s.d. Data were analysed using SPSS version 19.0 statistical package (SPSS, Chicago, IL, USA). Data were evaluated by linear regression analysis and correlations were assessed using Pearson correlation coefficients. For non-parametric data, the differences between two groups were analysed using the Mann–Whitney U-test. P-values<0.05 were considered statistically significant.
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