We performed descriptive statistics to determine the patient and donor characteristics. For continuous variables, Student’s t-test and an analysis of variance (ANOVA) were utilized. For categorical variables, we used Fisher’s exact test and the Chi-square test with Yates continuity correction if applicable. Pearson’s correlation and ANOVA were used to analyze trends and differences between the groups. Cumulative survival was determined using a Kaplan-Meier analysis, and the groups were compared using a log-rank test. Factors predictive of long-term survival were determined via Cox proportional hazards modeling, and the results are given in terms of hazard ratios (HRs). Only the variables found to be significant on univariate analysis were included for Cox proportional hazard modeling. All analyses were performed with the Statistical Package for the Social Sciences (SPSS 22, IBM Corp., Armonk, NY, USA) and Statistical Analysis System (SAS®, SAS Institute Inc. Cary, NC, USA). A P-value ≤ 0.05 was considered to indicate statistical significance.
Statistical package for the social sciences spss 22
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Sourced in United States
SPSS 22.0 is a statistical software package designed for data analysis. It provides a comprehensive set of tools for data manipulation, analysis, and visualization, catering to the needs of researchers and professionals in the social sciences and related fields.
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Lab products found in correlation
8 protocols using statistical package for the social sciences spss 22
1
Survival Analysis of Patient Characteristics
2
Sensory Processing Disorder Characteristics
3
Survival Analysis of Patient Cohort
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Baseline characteristics of patients were analyzed using Student's t-tests for continuous variables, χ2 tests for categorical data, and Mann–Whitney U test for the serum level of cytokines. Overall survival (OS) time was measured from the date of diagnosis to the date of death or to the last follow-up. Progression-free survival (PFS) was calculated from the date when the treatment began to the date when the disease progression was recognized or the date of the last follow-up as described previously [19 (link), 20 (link)]. Survival functions were estimated using the Kaplan-Meier method and compared by the log-rank test. Univariate hazard estimates were generated with unadjusted Cox proportional hazards. Multivariate survival analysis was performed using a Cox regression model in which significant variables in the univariate analysis were included. p < 0.05 was considered statistically significant. All statistical analyses were carried out using Statistical Package for the Social Sciences (SPSS) 22.0 software (SPSS Inc., Chicago, IL, USA).
4
Fetal Growth and Macronutrient Intake
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Statistical analysis of the baseline characteristics was performed using Student's t-test for continuous data and chi-square test for categorical variables. A series of linear regression models were performed to investigate the association between fetal growth and the multilocus score as independent variables on the intake of the different macronutrients during the Snack Test, adjusting for BMI at the time of the test and sex. IUGR was analyzed as a categorical variable (normal birth weight or IUGR). Additional analyses were performed adjusting for ethnicity (children classified as Caucasians and non-Caucasians). Data were analyzed using the Statistical Package for the Social Sciences (SPSS) 22.0 software (SPSS Inc., Chicago, IL, USA). Significance levels for all measures were set at p< 0.05.
5
Evaluating MBEC Impact on Resident Mental Health
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All data analysis was performed using the IBM Statistical Package for the Social Sciences (SPSS) 22.0 (SPSS Inc., Chicago, IL, USA). Senior residents’ characteristics and outcome variables were first depicted by descriptive statistics. Categorical Chi-square tests and independent t-tests were used to compare differences in characteristics between the two groups. To evaluate the effects of the intervention, a Linear mixed model (LMM) was employed. This model accounts for both within-group changes over time (main effects) and differences in these changes between the MBEC group and control group across time (interaction effects), exhibiting robust while handling missing data. The LMM primarily examined the interaction effects between the MBEC and control groups, along with the trajectory of change in outcomes of mental health and spiritual well-being over the course of the intervention and 4-week follow-up. The interaction between group (MBEC versus control) and time (baseline, mid-intervention, post-intervention, and 4-week follow-up) offered insights into the differential impact of the MBEC program compared to the control condition over time. Additionally, effect sizes were calculated for the outcome measures as well as the statistical significance. A p-value under 0.05 was considered statistically significant.
6
Survival Analysis of PiZZ Individuals
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The IBM Statistical Package for the Social Sciences (SPSS 22.0) software was employed for the statistical analyses. Cumulative, crude survival probabilities were estimated using life table. A standardized mortality ratio (SMR) was calculated as the ratio of observed to expected deaths in order to compare the death rates in the PiZZ individuals with the general Swedish population. The expected numbers of deaths were obtained using age-, sex-, and date-specific death rates published in Sweden annually (Statistics Sweden). Confidence intervals for the SMR were computed from the Poisson distribution. A P-value of <0.05 was considered significant.
7
ECMO Flow and Patient Oxygenation Correlation
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As we did not expect a particular probability distribution of the individual data series, we utilized the known impact of ECMO flow on patient oxygenation for an explorative analysis. The first day of support (Day 1) and 2–3 days before weaning (Day x) were chosen as the representative values for a non-parametrical Spearman rank correlation test to reveal an association between QECMO and SpO2 (first analysis day 1, second analysis day x). We postulated SpO2 to be independent of ECMO flow. Based on the different interindividual flow ranges, selecting five evenly distributed ECMO flow values was required from each patient data set for the correlation test. A repeated analysis at two different days should have shown whether the correlation could be confirmed during the entire ECMO period. A p-value < 5% was defined as significant. All data were collected as worksheets and analysed with Stata/IC 12.1 (Stata Corp., College Station, TX, USA) and Statistical Package for the Social Sciences (SPSS) 22.0 (IBM Corp., Armonk, NY, USA). Data graphing was carried out with OriginPro 2023 (OriginLab, Additive GmbH, Friedrichsdorf, Germany). To model a correlation between recirculation, gas exchange and patient oxygenation, we applied a curve fitting to the data and included the fitting line as well as its 95% CI in the graphs as appropriate.
8
Correlation of Meibomian Gland Dysfunction with Ocular Symptoms
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Descriptive statistics were calculated for the age of the subjects, including mean, standard deviation, median, and range. Data on the OSDI were analyzed using the Kolmogorov–Smirnov test for normality and nonparametric tests were chosen accordingly. Scatter diagrams were constructed to visualize the ordinal data. Spearman’s correlation coefficient was used to determine the correlation between the symptom score, sign score, and Meiboscale. A P value <0.05 was considered statistically significant, and rs > 0.5 as clinically significant.[11 (link)] There were no missing data. All tests were conducted on the software Statistical Package for the Social Sciences (SPSS, 22.0; IBM Corporation, New York, USA). The Jonckheere–Terpstra (J–T) test was applied to evaluate the distribution of OSDI in patients with varying ordinal MGD scores and MGL scores. The r-effect from the J–T test was used to evaluate the effect size from the analysis. Sensitivity analysis and subgroup analysis between symptomatic and asymptomatic MGD was beyond the scope of this study.
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