The unpaired student’s t-test and the Mann–Whitney U test were used to analyze normally and non-normally distributed continuous data, which were expressed as mean with standard deviation and median with interquartile range (IQR: Q1–Q3), respectively. Categorical data were expressed as number and frequency (%) and were compared using two-sided Fisher’s exact or Pearson’s chi-square tests with the presentation of odds ratios (ORs) with 95% confidence intervals (CIs). To minimize the confounding effects of sex, age, and injury severity of patients on outcome measurements, a logistic regression model was used to calculate the propensity scores with the following covariates: sex, age, and ISS. Subsequently, 1:1-propensity score-matched patient populations were established using the Greedy method with a 0.2-caliper width using the NCSS 10 software (NCSS statistical software, Kaysville, UT, the USA) for the assessment of the impact of SIH compared with NDN on the outcomes. All statistical analyses were performed using the Statistical Package for the Social Sciences software for Windows version 22 (IBM Corp., Armonk, NY, the USA). p-values less than 0.05 were considered statistically significant.
Ncss 10
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NCSS 10 is a comprehensive statistical software package that provides advanced analytical tools for data analysis and modeling. It offers a wide range of statistical procedures, including regression, ANOVA, survival analysis, and more. The software is designed to be user-friendly and provides a intuitive interface for data management, visualization, and report generation.
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Lab products found in correlation
Spss statistics for windows version 20, by IBM (2 mentions)
Spss statistics for windows version 23, by IBM (1 mentions)
Spss 23.0 software for windows, by IBM (1 mentions)
Spss for windows version 22, by IBM (1 mentions)
Spss statistics v22.0 software for windows, by IBM (1 mentions)
Spss for windows version 23, by IBM (1 mentions)
Spss statistics for window, by IBM (1 mentions)
Spss software version 19, by IBM (1 mentions)
12 protocols using ncss 10
1
Comparing Trauma Outcomes with Statistical Methods
2
Postoperative Outcomes: Statistical Analysis
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Statistical analyses were conducted using IBM SPSS Statistics for Windows, version 23.0 (IBM Corp., Armonk, NY, USA) and NCSS 10 software (NCSS Statistical Software, Kaysville, UT, USA). Categorical data were compared using the X2-test and Fisher’s exact test. We verified the normality of distribution for continuous variables with the Kolmogorov–Smirnov test. For normally distributed data, we expressed continuous variables as means with standard deviation, while for non-normally distributed data, we used the median with the interquartile range. We utilized ANOVA analyses and Kruskal- Wallis tests to analyze normally and non-normally distributed continuous variables between groups. We conducted univariate logistic regression analyses to examine the risk factors for major complications and prolonged postoperative length of stay. Variables that were significantly associated in the univariate analyses were then entered into the multivariate logistic regression to control for potential confounding. All tests were two-sided, and a P-value less than 0.05 was considered statistically significant.
3
Minimally Invasive vs. Open Surgery Outcomes
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Statistical analyses were performed using IBM SPSS Statistics for Windows, version 20.0 (IBM Corp., Armonk, NY, USA) and NCSS 10 software (NCSS Statistical Software, Kaysville, UT, USA). Two-sided Fisher’s exact or Pearson chi-square tests were used to compare categorical data. The normally distributed continuous and non-normally distributed data were analyzed with unpaired Student’s t- and Mann–Whitney U-tests, respectively, and presented as mean ± standard deviation or median with interquartile rage (IQR).
To minimize the potential confounding effects of the compared patient populations due to non-randomized assignment, a 1:2 PSM study group (MIS vs. Open surgery) was created using the Greedy method with a 0.2 caliper width using NCSS 10 software. The PSM analysis was performed using a logistic regression model with the following covariates: Age, sex, malignancy status, and CCI. After adjusting for these confounding factors, binary logistic regression analysis was used to evaluate the effect of minimally invasive and open surgery on postoperative recovery. Statistical significance was set at a P-value of < 0.05 for each analysis.
To minimize the potential confounding effects of the compared patient populations due to non-randomized assignment, a 1:2 PSM study group (MIS vs. Open surgery) was created using the Greedy method with a 0.2 caliper width using NCSS 10 software. The PSM analysis was performed using a logistic regression model with the following covariates: Age, sex, malignancy status, and CCI. After adjusting for these confounding factors, binary logistic regression analysis was used to evaluate the effect of minimally invasive and open surgery on postoperative recovery. Statistical significance was set at a P-value of < 0.05 for each analysis.
4
Mortality Outcomes in Neurogenic Dysphagia
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Statistical analyses were performed using SPSS 23.0 software for Windows (IBM Corp., Armonk, NY, USA). In each hospital (Keelung, Linkou, Chiayi, Kaohsiung), comparisons were made among the four patient groups (NDN, DN, SIH, DH). The continuous variables were analyzed using one-way analysis of variance following Games-Howell post-hoc tests and were expressed as mean ± standard deviation. Two-sided Fisher’s exact or Pearson’s chi-squared (χ2) tests were used to compare categorical data, with odds ratios (ORs) being calculated with 95% confidence intervals (CIs). To minimize the confounding effects of baseline characteristics between patient populations when assessing mortality outcomes, a 1:1 propensity score-matched study group was created by the Greedy method with a 0.2 caliper width using NCSS 10 software (NCSS Statistical Software, Kaysville, Utah). The propensity scores were calculated using a logistic regression model, including the following covariates: sex, age, and GCS score. After adjusting for these confounding factors, Cox regression was used to evaluate the effects of SIH and DH on the primary outcome compared to NDN. Additionally, this method was used to examine differences between the SIH and DH patient groups. The primary outcome measure was in-hospital mortality. A p value of < 0.05 was set to determine statistically significant group differences.
5
Mortality Risk Factors After Injury
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The Kolmogorov–Smirnov test was used to assess the normalization of the distributed data for continuous variables. The unpaired Student’s t-test and Mann–Whitney U-test were used to analyze normally and non-normally distributed continuous data, respectively. The results are expressed as mean ± standard deviation, with ISS presented as median and interquartile range (IQR, Q1–Q3). The categorical data were compared using two-sided Fisher’s exact or Pearson’s χ2 test. Univariate predictive variables resulting in mortality of the patients were identified, and multivariate logistic regression analysis was used to identify the independent risk factors for mortality, with the presentation of odds ratios (ORs) and 95% confidence intervals (CIs). In this study, all statistical analyses were performed using Windows version 23.0 for SPSS (IBM Inc., Chicago, IL, USA). To minimize the confounding effects of sex, age, comorbidities, and injury severity of patients on outcome measurements due to a nonrandomized assignment of the study population, a logistic regression model was used to calculate the propensity scores with the aforementioned covariates and then, 1:1 propensity score-matched patient populations were created using the NCSS 10 software (NCSS statistical software, Kaysville, UT, USA) with the greedy method. P values <0.05 were considered statistically significant.
6
Mild Hypoalbuminemia and Surgical Outcomes
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For our analyses, “control” was defined by a normal serum albumin levels (> = 35 g/L), and “case” was defined by mild hypoalbuminemia (< 35 and > =30 g/L) as measured by laboratory data collected within 2 weeks before surgery. Individual propensity scores were calculated by logistic regression method based on 21 clinical associated factors including age, gender, body mass index, smoking, emergent operation and other comorbidities. A 1:2 ratio propensity score matching study group was created using the Greedy method with a 0.2 caliper width using NCSS 10 software (NCSS Statistical Software, Kaysville, UT, USA) [25 (link)]. A chi-square test was used for univariate association. Binary logistic regression method was used to analyze the association between mild hypoalbuminemia and postoperative mortality and morbidity. The association among length of total hospital stay, overall morbidity and mild hypoalbuminemia was analyzed with regression analysis. Tests were two-tailed and statistical significance was defined at p < 0.05. All statistical analyses were performed on SPSS for Windows version 22.
7
Propensity Score-Matched Aspirin Effects
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Categorical data, such as sex, comorbidities, lifestyle risk factors, AJCC stages of cancer, etc., were tested by either a two-sided Fisher’s exact test or a Pearson’s chi-squared test. The normally and non-normally distributed continuous data were analyzed using Student’s t-tests and Mann–Whitney U tests, respectively. In order to minimize the confounding effect of groups that are comparable due to non-randomized allocations, a 1:4 propensity score-matched study group (aspirin user vs. non-user) was created using the Greedy method with a 0.25 caliper-width using NCSS 10 software (NCSS Statistical Software, Kaysville, UT, USA) (Table S1 ) [41 (link),42 (link),43 (link),44 (link),45 ,46 (link),47 (link)]. The propensity scores were calculated using a logistic regression model with the following covariates: sex, age and AJCC stages of cancer. After adjusting for these confounding factors, the Kaplan–Meier method was used to evaluate the effects of aspirin use in the primary outcome (DSS). A univariate analysis and Cox proportional-hazards model were used to evaluate any parameters that could affect survival. All statistical analyses were performed using SPSS Statistics V22.0 software for Windows (IBM Corp., Armonk, NY, USA). Statistical significance was set for each analysis at p-values of <0.05.
8
Admission Hyperglycemia Outcomes in Trauma
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We used Statistical Package for the Social Sciences Statistics (SPSS) for Windows, version 23.0 (International Business Machines Corporation, Armonk, NY, USA) for the statistical analysis. The categorical data were analyzed using Pearson chi-squared tests or a two-sided Fisher’s exact test with the presentation of odds ratios (ORs) and 95% confidence intervals (CIs). We used Levene’s test to test the homogeneity of the variance of continuous data. Subsequently, we used a one-way analysis of variance (ANOVA) with the Games–Howell post hoc test to assess the differences of continuous variables among different groups of patients. The continuous data were presented as mean ± standard deviation. The value of ISS was expressed as the median with an interquartile range (Q1–Q3); p-values <0.05 were considered statistically significant. The primary outcome of the study was in-hospital mortality. To attenuate the confounding effects of sex, age, comorbidities, and ISS of patients in assessing the mortality rate, different 1:1 propensity score-matched patient populations were established by the NCSS 10 software (NCSS Statistical Software, Kaysville, UT, USA) using the Greedy method in a 0.2 caliper width for the assessment of the impact of admission hyperglycemia (SIH or DH) vs. NDN, as well as SIH vs. DH, on the mortality outcomes.
9
Intracranial Hemorrhage Outcomes Analysis
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We performed the statistical analyses using IBM SPSS Statistics for Windows, version 20.0 (IBM Corp., Armonk, NY, USA) and NCSS 10 software (NCSS Statistical Software, Kaysville, UT, USA). The primary outcome of the study was in-hospital mortality. Two-sided Fisher’s exact or Pearson chi-square tests were used to compare categorical data. Odds ratios (ORs) with 95% CIof the associated conditions of the patients were presented. The normally distributed continuous and non-normally distributed data were analyzed with unpaired Student’s t- and Mann–Whitney U-tests, respectively, and presented as mean ± standard deviation. To minimize the confounding effects of the baseline characteristics of the compared patient populations due to a non-randomized assignment, a 1:1 propensity score-matched study group was created using the Greedy method with a 0.2 caliper width using NCSS 10 software to assess the effect of SIH and DH on the outcomes. The propensity scores were calculated using a logistic regression model with the following covariates: sex, age, co-morbidities, types of intracranial hemorrhage, and ISS. After adjusting for these confounding factors, cox regression was used to evaluate the effects of SIH and DH on the primary and secondary outcomes against those of NDN. p-values < 0.05 were defined as statistically significant.
10
Predictive Factors for Mortality in Trauma
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All statistical analyses were performed using IBM SPSS Statistics for Windows (version 23.0; IBM Corp., Armonk, NY, USA). Two-sided Fisher exact or Pearson chi-square tests were used to compare the categorical data by calculating the odds ratio (OR) and 95% confidence interval (CI). Levene’s test was used to assess the homogeneity of variance in the continuous variables. Normally distributed continuous data were analyzed using unpaired Student’s t-test and are presented as mean ± standard deviation. Non-normally distributed data, such as GCS and ISS, were analyzed using the Mann–Whitney U-test and are presented as the median and interquartile range (IQR, Q1–Q3). To minimize the confounding effects of the baseline covariates that may be related to the assessment of MLR, NLR, PLR, and WBC subpopulations, propensity scores were estimated using multiple logistic regression analysis with adjustments for patient sex, age, pre-existing comorbidities, and ISS, and a 1:1 matched study group was created using the greedy method and a 0.2 caliper width using NCSS software (NCSS 10; NCSS Statistical software, Kaysville, UT, USA). Univariate predictive variables that resulted in patient mortality were identified, and multivariate logistic regression analysis was used to identify independent risk factors for mortality. Statistical significance was set at p < 0.05.
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