Spss pasw 18, by IBM - Product details

1

Surgical Outcomes and Complications Analysis

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All analyses were performed using SPSS (PASW) 18.0 software (SPSS Inc., Chicago, Illinois, USA). Multivariable binary logistic regression was used to estimate the relationship between year of surgery and clinical outcome, correcting for age, gender, BMI at inclusion, hypertension, diabetes, dyslipidemia and type of surgery. Multivariate analysis was used to evaluate the differences in minor and major complication rates between the different types of procedures, corrected for surgeon, baseline characteristics and type of procedure. Multivariate analysis was also used for comparing the percentages of patients revisiting the hospital without having a complication over the years, correcting for the same covariates. Results were evaluated at a significance threshold of p < 0.05 (two-sided).

Leeman M., van Mil S.R., Biter L.U., Apers J.A., Verhoef K, & Dunkelgrun M. (2021). Reducing complication rates and hospital readmissions while revising the enhanced recovery after bariatric surgery (ERABS) protocol. Surgical endoscopy, 35(2).

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2

Obesity and Cardiometabolic Profiles

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All analyses were performed using SPSS (PASW) 18.0 software (SPSS Inc., Chicago, IL, USA).
Continuous variables were presented as mean ± standard deviation (SD). Due to non-Gaussian distribution, both TG and CRP are described as median and minimum-maximum. Categorical data were described as an absolute number as well as a percentage of the total cohort.
Differences between obese and non-obese subjects were analyzed using independent T tests, chi-squared tests, and Kruskal-Wallis tests.
The relationship between BMI quintiles and metabolic and inflammatory parameters was analyzed using one-way ANOVA or the Kruskal-Wallis test in case of non-Gaussian distribution.
Patients on statins were excluded from the analyses on the relation between BMI and total cholesterol, HDL-C, LDL-C, TG, C3, CRP, and apoB. Subjects on antihypertensive drugs were excluded from analyses on the relation between BMI and systolic and diastolic blood pressure. Subjects on glucose-lowering drugs were excluded from the analysis on the relation between BMI and glucose. Pearson's correlation coefficients were calculated in order to analyze the relationship of BMI with the different adipose tissue surfaces. Results were evaluated at a 95% confidence interval at a significance threshold of p < 0.05 (two-sided).

van Mil S.R., Vijgen G.H.E.J., van Huisstede A., Klop B., van de Geijn G.M., Birnie E., Braunstahl G.J., Mannaerts G.H.H., Biter L.U, & Castro Cabezas M. (2018). Discrepancies Between BMI and Classic Cardiovascular Risk Factors. Obesity surgery, 28(11).

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3

Semantic Clustering and Recall Strategies

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Total recall scores were calculated by summing the number of words correctly recalled in the Related uncued and Related Cued word conditions separately. Total semantic clustering scores were also calculated as objective measures of semantic categorization strategy use. Observed semantic clustering scores for each related word list were calculated by summing the number of semantic clusters during recall. A semantic cluster occurred whenever a participant recalled two words in succession from the same semantic category. These clustering scores were adjusted for chance [Observed Expected: (# Clusters -- (# Clusters / 4.23))], and averaged across the two lists for each instruction type (uncued and Cued) [30 (link)]. Total recall and total semantic clustering scores were entered into Instruction (uncued, Cued) by Training (Pre-training, Post-training) repeated measures ANOVAs in SPSS/PASW 18 (IBM Corporation, Somers, NY).

Lepping R.J., Brooks W.M., Kirchhoff B.A., Martin L.E., Kurylo M., Ladesich L., Lierman J.A., Varghese G, & Savage C.R. (2015). Effectiveness of Semantic Encoding Strategy Training after Traumatic Brain Injury is Correlated with Frontal Brain Activation Change. International journal of physical medicine & rehabilitation, 3(1 Spec Iss), 254.

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4

Behavioral Assessment of Inflammation

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All data was analysed using SPSS (PASW) 18 (IBM). For behavioural experiments, logarithmic transformation (log2) was carried out on the paw withdrawal thresholds (g) before statistical analysis as Levene’s test for equality of variance was significant [69 (link),70 (link)]. Analysis of variance (ANOVA) with repeated measures was used for all time course experiments. To proceed to further post hoc testing at least one main effect (CFA treatment or toxin) or interaction was required to be statistically significant (p < 0.05). Subsequent two-way ANOVA with repeated measures was carried out to determine if there was an overall effect of CFA or toxin across a specific time window. For cell count experiments and RT-qPCR experiments independent samples t-tests were used to compare means between two groups. In all tests a p-value of < 0.05 was deemed significant.

Carr F.B., Géranton S.M, & Hunt S.P. (2014). Descending controls modulate inflammatory joint pain and regulate CXC chemokine and iNOS expression in the dorsal horn. Molecular Pain, 10, 39.

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5

Semantic Clustering and Recall Strategies

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Total recall scores were calculated by summing the number of words correctly recalled in the Related uncued and Related Cued word conditions separately. Total semantic clustering scores were also calculated as objective measures of semantic categorization strategy use. Observed semantic clustering scores for each related word list were calculated by summing the number of semantic clusters during recall. A semantic cluster occurred whenever a participant recalled two words in succession from the same semantic category. These clustering scores were adjusted for chance [Observed Expected: (# Clusters -- (# Clusters / 4.23))], and averaged across the two lists for each instruction type (uncued and Cued) [30 (link)]. Total recall and total semantic clustering scores were entered into Instruction (uncued, Cued) by Training (Pre-training, Post-training) repeated measures ANOVAs in SPSS/PASW 18 (IBM Corporation, Somers, NY).

Lepping R.J., Brooks W.M., Kirchhoff B.A., Martin L.E., Kurylo M., Ladesich L., Lierman J.A., Varghese G, & Savage C.R. (2015). Effectiveness of Semantic Encoding Strategy Training after Traumatic Brain Injury is Correlated with Frontal Brain Activation Change. International journal of physical medicine & rehabilitation, 3(1 Spec Iss), 254.

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6

Validating the Quality of Dying Hospice Measure

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PASW SPSS 18.0 (SPSS Inc., Chicago, IL) was used for statistical analyses. Data were prescreened for outliers and missing values. Missing items were substituted using the expectation maximization method appropriate for validity testing.^{27 (link),28 (link)} Construct validity of the QOD-Hospice was evaluated with: 1) a convergent test examining associations with conceptually related measures – the LSNS-6, DASS-21, TRIG-2, and care satisfaction; 2) an exploratory factor analysis (EFA) exploring the measure’s structure. EFA was deemed appropriate despite the limited sample size.²⁹ To determine the number of factors, the study used parallel analysis with direct oblimin rotation.³⁰ For factor selection, we used a conservative loading threshold of 0.50 to ensure parsimony and interpretability.³¹ To examine measurement reliability, the following estimates were calculated: Cronbach’s alpha for internal consistency; percentage agreement (i.e., the proportion of scale scores falling within one standard deviation among caregivers of the same decedent) and intraclass correlation (ICC) for inter-rater reliability among caregivers of the same decedent.

Cagle J.G., Munn J.C., Hong S., Clifford M, & Zimmerman S. (2014). Validation of the Quality of Dying (QOD)-Hospice Scale. Journal of pain and symptom management, 49(2), 265-276.

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7

Statistical Analysis of Patient Characteristics

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Patient baseline characteristics are expressed as median (range) for continuous data, and as numbers with percentages for categorical data. Fisher’s exact test was used to compare differences in categorical variables, and the Wilcoxon rank sum test for continuous variables. Variables achieving statistical significance at the 0·1 level in univariable analysis were considered for multivariable analysis. A backward variable procedure was used to identify independent predictive factors. A P value of 0.05 was considered statistically significant and odds ratios (OR) with 95%CI were calculated. All statistical analyses were performed with PASW (SPSS) 18.0 (SPSS Inc, Chicago, IL, United States).

Zarzavadjian Le Bian A., Fuks D., Chopinet S., Gaujoux S., Cesaretti M., Costi R., Belgaumkar A.P., Smadja C, & Gayet B. (2017). Consequences of metabolic syndrome on postoperative outcomes after pancreaticoduodenectomy. World Journal of Gastroenterology, 23(17), 3142-3149.

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8

Descriptive Statistical Analysis of OS

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Results were analyzed using the Statistical Package for the Social Sciences (SPSS PASW18). A p < 0.05 was considered statistically significant. OS was calculated in our study at one year with Stata/IC 15.0 program. Given that the objective of the study was merely descriptive, and therefore, there was not hypothesis to be confirmed, the sample estimation prior to the study was not necessary.

Escamilla Gómez V., García-Gutiérrez V., López Corral L., García Cadenas I., Pérez Martínez A., Márquez Malaver F.J., Caballero-Velázquez T., González Sierra P.A., Viguria Alegría M.C., Parra Salinas I.M., Calderón Cabrera C., González Vicent M., Rodríguez Torres N., Parody Porras R., Ferra Coll C., Orti G., Valcárcel Ferreiras D., De la Cámara LLanzá R., Molés P., Velázquez-Kennedy K., João Mende M., Caballero Barrigón D., Pérez E., Martino Bofarull R., Saavedra Gerosa S., Sierra J., Poch M., Zudaire Ripa M.T., Díaz Pérez M.A., Molina Angulo B., Sánchez Ortega I., Sanz Caballer J., Montoro Gómez J., Espigado Tocino I, & Pérez-Simón J.A. (2019). Ruxolitinib in refractory acute and chronic graft-versus-host disease: a multicenter survey study. Bone Marrow Transplantation, 55(3), 641-648.

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9

Caval Index Predicts Fluid Response

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The statistical analysis was performed with SPSS PASW-18. Baseline characteristics in responders and non-responders were compared using the Mann Whitney U test, and for nominal variables using the Fisher exact test. The Wilcoxon signed rank test was used to compare paired values before and after fluid therapy. Linear correlation between the change in systolic blood pressure and the caval index was tested using the Spearman Rank method. A receiver operating characteristic (ROC) curve was plotted to determine the threshold value of the caval index which provided the prediction of the response to fluid therapy with the best sensitivity and specificity. A P-value less than 0.05 was considered statistically significant.

de Valk S., Olgers T.J., Holman M., Ismael F., Ligtenberg J.J, & ter Maaten J.C. (2014). The caval index: an adequate non-invasive ultrasound parameter to predict fluid responsiveness in the emergency department?. BMC Anesthesiology, 14, 114.

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Spss pasw 18

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9 protocols using spss pasw 18

Surgical Outcomes and Complications Analysis

Obesity and Cardiometabolic Profiles

Semantic Clustering and Recall Strategies

Behavioral Assessment of Inflammation

Semantic Clustering and Recall Strategies

Validating the Quality of Dying Hospice Measure

Statistical Analysis of Patient Characteristics

Descriptive Statistical Analysis of OS

Caval Index Predicts Fluid Response

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