The difference of age distribution between the case group and the control group was compared by Student’s t-test. The Hardy-Weinberg equilibrium (HWE) of the control group was checked by the chi-square test. The association of these 42 tSNPs with susceptibility to HIV-1 infection and AIDS progression were determined using the chi-square test, and the effect size was expressed as an odds ratio (OR) with 95% confidence interval (95% CI). Haploview v4.2 was used to analyze the LD and haplotypes, and 10,000 permutations were run to compute P values. Generalized Multifactor Dimensionality Reduction (GMDR) v 0.9 (http://www.ssg.uab.edu/gmdr/ ) was utilized to explore the possible gene-gene interaction. Association between the number of risk alleles and AIDS was calculated using logistic regression. The statistical analyses were performed by SPSS v. 22.0 statistical software (IBM-SPSS, Inc., Chicago, USA) and R statistical software (v3.6.3). P < 0.05 was considered statistically significant.
Spss v 22.0 statistical software
Manufactured by IBM
Sourced in United States
SPSS V.22.0 is a statistical software package designed for data analysis and management. It provides a range of tools for data manipulation, statistical modeling, and reporting. The software is intended to assist users in analyzing and interpreting complex data sets.
Automatically generated - may contain errors
Lab products found in correlation
21 protocols using spss v 22.0 statistical software
1
Genetic Susceptibility to HIV-1
2
Statistical Analysis of Experimental Data
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The IBM SPSS v.22.0 statistical software (SPSS, Chicago, IL, USA) was used for data analysis, and evaluation of significant treatment effects was performed using Duncan's multiple range test (DMRT). OriginPro v.2023b was used for graph drawing. The data are described as the mean ± SD. A value of p < 0.05 was considered as the significance limit for all comparisons.
3
Randomized Sham-Controlled Acupuncture Trial
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The participants will be randomised in a ratio of 1:1 to the acupuncture group, and the sham acupuncture group. To avoid selection bias, random numbers were generated using SPSS V.22.0 statistical software (SPSS, Chicago, Illinois, USA) and sealed in opaque envelopes. To avoid selection bias, random numbers will be generated by an independent research assistant using a computer and sealed in opaque envelopes. After participants accept the principle of random allocation, they will randomly conduct to select an opaque envelope and obtain an allocation sequence number, which will be recorded into a case report form (CRF) by a specially assigned person. The participants, researchers, outcome assessors and statisticians will be blinded to the group allocation throughout the study. Because of the particularity of the sham acupuncture technique, acupuncturists could not be blinded.
4
Statistical Analysis of Repeated Measurements
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In this study, all statistical analyses were conducted using SPSS v.22.0 statistical software (SPSS Institute Ltd, Armonk, NY, United States), and a general mean ± standard error across repeated measurements was calculated and presented. A two-way analysis of variance (ANOVA) and LSD (least significant difference) tests were used to compare the significance of the differences between the control and treatments. Meanwhile, one-way ANOVA and Duncan’s multiple range comparison tests were used to analyze qRT-PCR data.
5
Haploidentical Hematopoietic Cell Transplantation Outcomes
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Differences in age, mononuclear cells (MNCs) and CD34+ cells in grafts, MMF durations, and cell counts of T-cell subpopulations at different time points between the groups were analyzed using the Mann-Whitney U test. Differences in gender, type of primary disease, and incidences of acute GVHD and viral infections were analyzed using the χ2 test. Cox regression analysis was performed to distinguish the independent association of T-subsets recoveries with EBV reactivation after haploHCT. Statistical significance was defined as p≤0.05, based on a two-tailed test. All calculations were performed using SPSS V.22.0 statistical software (SPSS).
6
Brachytherapy Response and Survival
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Pre- and post- operative D90, V100, and CI were compared using a paired t-test or non-parametric Wilcoxon test. Overall response rate (ORR), disease control rate (DCR) at 6 months, and 1-year survival rate were calculated. ORR was calculated as CR + PR/all evaluated patients. DCR was calculated as CR + PR + SD/all evaluated patients. Median overall survival (OS) measured from the date of 125I seed brachytherapy was estimated by Kaplan-Meier method. Statistical analysis was performed by SPSS v. 22.0 statistical software. P-value < 0.05 was considered statistically significant.
7
Diabetes Markers and Exercise Physiology
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Data were tested for normal distribution via Shapiro-Wilk test. Descriptive statistics included mean and standard deviation for participant’s characteristics. kHR was calculated by a second-degree polynomial representation between post-warm up and Pmax. Stepwise linear regression was used to explore relationships between kHR and diabetes markers (glycemic control (HbA1c), total daily dose of insulin (both basal- and bolus insulin), duration of diabetes), anthropometry (height, weight, body mass index (BMI)) and physical activity (IPAQ). Stepwise linear regression was also used between kHR and exercise physiological markers (CPX derived cardio-respiratory markers at HRTP and at Pmax). Stepwise linear regressions were adjusted for gender, BMI, physical activity, total daily dose of insulin, duration of diabetes and blood glucose concentration at the start of CPX testing if not included in the regression model. Logarithmic transformation was performed if data were non-normally distributed. Receiver operating characteristic (ROC) curves based on kHR for groups kHR < 0.20 vs. > 0.20 in relation to HbA1c. All statistical analyses were carried out using SPSS V.22.0 statistical software (SPSS, Chicago, Illinois, USA). A sample size of 64 individuals with T1DM resulted in a power (1 –β error probability) of 1.0 for the main outcome analyzed via stepwise linear regression, respectively.
8
Statistical Analysis of ARNI and Atrial Arrhythmia
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Data are presented as mean ± standard deviation (SD) or mean ± standard error of the mean (SEM) for continuous variables and as frequency and percentages for nominal variables. Normally distributed continuous variables were compared using the Student’s t-test. The Pearson χ2 test was applied to all categorical variables. Logistic regression analysis was performed to evaluate the relation between ARNI and atrial arrhythmia at 24-week follow-up. A P-value of <0.05 was considered to be statistically significant. All statistical analyses were performed with SPSS v22.0 statistical software (SPSS, Chicago, IL, USA) and Prism 8.0 (GraphPad, San Diego, CA, USA).
9
Bone Mineral Density and Body Composition
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The SPSS v22.0 statistical software is used for data analysis, and the measurement data are expressed by mean ± standard deviation (± s). The two samples were compared by t-test. Spearman was used to analyze the correlation between body composition and bone mineral density. The relationship between bone mineral density and body composition was analyzed by linear regression. The difference was statistically significant (P < 0.05).
10
Smoking Cessation Predictors Analysis
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Descriptive statistics were used to determine the prevalence of smoking and potential risk factors for continued smoking and quitting. Chi-square tests for nominal variables and ANOVA tests for continuous variables were used to assess the bivariate relationships between risk factors and smoking outcomes, unadjusted for potential confounding variables.
Two multivariable logistic regression models were used to assess the odds of continued smoking versus quitting and the odds of continued smoking versus never smoking incorporating the socio-demographic and health behaviour measures into the model. Analyses were performed using SPSS v22.0 statistical software.
Two multivariable logistic regression models were used to assess the odds of continued smoking versus quitting and the odds of continued smoking versus never smoking incorporating the socio-demographic and health behaviour measures into the model. Analyses were performed using SPSS v22.0 statistical software.
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