Results of NGSNP testing directly on residual N.gonorrhoeae-positive NAAT lysates was compared with findings of routine culture-based AST. A sample size of 220 prospective clinical episodes was determined to be required in order to assess the performance of NGSNP with 98% accuracy at a 95% CI of 95%–100% (β = 80%, α = 0.05). CIs were calculated using a Wilson score, assuming a binomial distribution, with MedCalc statistical software (MedCalc, Ostend, Belgium). χ2 tests were used to detect differences in the failure rate of NGSNP and prevalence of ciprofloxacin resistance at different sampling sites. Statistical analysis was performed using SPSS version 21 and MedCalc statistical software.
Spss version 21
Manufactured by MedCalc
SPSS version 21 is a statistical software package designed for data analysis. It provides tools for data management, analysis, and presentation. The software enables users to perform a variety of statistical procedures, including descriptive statistics, bivariate analysis, regression, and multivariate analysis.
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Lab products found in correlation
8 protocols using spss version 21
1
NGSNP Testing for Gonococcal AST
2
Evaluating Intervention Outcomes: A Comprehensive Analysis
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Descriptive and infererntial statistics were used to analyse the data. Continous variables were summarized using mean and standard deviation (SD) or median and inter quartile range (IQR) based on normality assumptions. Normal continous varibles were compared between the intervention and control groups, using independent t-test while those that were not normal were analysed using Mann Whitney U test. Categorical variables were described using frequency and percentages. Testing of hypotheses of categorical varibles were evaluated using Chi-square test or Fisher’s exact test as appropriate.
Primary and secondary outcome variables were compared by computing the risk in each group and risk ratio (RR) and 95% confidence interval (CI). We did not expect statistical signficance between the groups for the oucome measures but point estimates (RR) were expected to show the direction and approximate magnitude of effect, if the study were to have been sufficienty powered. With the intention of conducting a larger trial, we used these data for a power calculation. Most data analyses were carried out using SPSS version 21 [21 ], MedCalc [22 ] was used for risk computations and confidence intervals, and PASS version 12 [23 ] was used for sample size calcuations.
Primary and secondary outcome variables were compared by computing the risk in each group and risk ratio (RR) and 95% confidence interval (CI). We did not expect statistical signficance between the groups for the oucome measures but point estimates (RR) were expected to show the direction and approximate magnitude of effect, if the study were to have been sufficienty powered. With the intention of conducting a larger trial, we used these data for a power calculation. Most data analyses were carried out using SPSS version 21 [21 ], MedCalc [22 ] was used for risk computations and confidence intervals, and PASS version 12 [23 ] was used for sample size calcuations.
3
Predictive Factors for Mortality Outcomes
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Categorical variables were reported as number, and statistical analyses were conducted using the χ2 test. Continuous variables were presented as median (P25–P75), and comparisons were made using the Mann–Whitney U test. Factors that exhibited potential associations with poor outcomes in the univariable analyses were included in the multivariate binary regression analysis. To assess the predictive value of these factors for mortality and compare the differences in their performance, we generated receiver-operator characteristic (ROC) curves and calculated the area under the curve (AUC) values. Statistical analyses were conducted using SPSS version 21 and MedCalc 12.7.0. Statistical significance was set at p < 0.05.
4
Statistical Analysis of Tumor Subtype Profiling
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Data collection and statistical analyses were performed operating with IBM SPSS Version 21 (SPSS Inc, Chicago, IL) and MedCalc Version 14 software (Medcalc bvba, Ostend, Belgium). Precise scaling for the different variables were expressed as median (and range), categorical parameters (cross-tabulation and percentages), and survival data (Kaplan-Meier method). For statistical testing, Spearman rank correlation and log rank test were employed. For hierarchical clustering and generation of heat-map images, Multi Experiment Viewer (MEV, www.tm4.org ) [29 ] was used. To generate MEV dataset, gene or antigen expression levels were expressed as relative values with the maximum level defined as 100 %, and samples were named according to tumor subtype (INT intestinal, PB pancreatobiliary, POOR poorly differentiated) or cell line. Upon loading to MEV, gene/row normalization and hierarchical clustering (HCL) was performed [30 (link)] and heat-map images of the HCL tree diagrams generated for visual interpretation.
5
Evaluating Biomarkers for Disease Risk
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According to the different data distribution, continuous variables were described as mean ± standard or median (Inter-quartile range, IQR), and groups were compared by student’s t-test or Mann-Whitney U test based on the data distribution. Categorical variables were presented as n (%) and analyzed by Pearson’s chi-square. Receiver operator characteristic (ROC) was used to evaluate the efficacy of NLR, LDH, D-dimer and CT score and get the optimum cutoff. Logistic regression was used to access the predictive value for disease risk. The statistical software needed is SPSS version 21 and Medcalc (version 19.1). A value of p<0.05 was considered statistically significant.
6
Predictive CT Severity Scoring
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Data were investigated with SPSS version 21 and MedCalc version 19.4.1 software. Normally distributed variables are expressed by mean ± standard deviation (SD) and categorical variables by percentages. The t-test was used to compare the continuous variables. As for the categorical variables the chi-squared test was used. For estimating the optimal cut-off score, a Receiver Operating Characteristics (ROC) curve analysis was performed (according to Youden’s index for maximizing sensitivity and specificity). Survival probability for CT severity score was estimated using the means of the Kaplan–Meier curves, with the endpoint being death. Cox proportional hazards regression was performed for both univariate and multivariate analyses. The P-value was considered significant when less than 0.05 in all analyses.
7
Quantitative CT Image Analysis Protocol
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All statistical analyses were performed with SPSS version 21.0 (Inc.Chicago, IL) and MedCalc (medcalc for Windows, version15.2, www.medcalc.org). All continuous values are expressed as mean ± SD. The repeated 1-way analysis of variance test was used to compare the average CT attenuation, image noise, artifact index, and CNR among different datasets, and if there was a significant difference, pairwise comparisons would be performed using the paired t test with the Bonferroni correction. Friedman test was used to compare subjective scores among images with different datasets, and if there was a significant difference, pairwise comparisons would be performed with the Steel–Dwass test. Interobserver agreement for subjective image scores was measured using Kappa test. McNemar test was used to compare the difference in lacunar lesion detection between the 3 groups of thin-slice images and routine slice images, respectively. A P < .05 was considered statistical significant.
8
Serological and Molecular Screening of Blood Donors
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Statistical analysis was conducted using SPSS version 21.0 and MedCalc version 19.8, with the help of an expert biostatistician. Descriptive analysis was performed to identify infection frequencies in donors using both datasets from the serological immunoassay and NAT screening. Statistical evaluations of various parameters in blood donor dataset were also performed to understand their mean values and general distributions in the study population.
Chi-squared cross-tabulation tests and kappa inter-rater correlation tests were used to assess these comparisons. p-values of ≤0.05 were considered statistically significant, with 95% confidence intervals for all correlation analyses. Any significant association between the prevalence of serological markers and NATs was explored without bias.
Chi-squared cross-tabulation tests and kappa inter-rater correlation tests were used to assess these comparisons. p-values of ≤0.05 were considered statistically significant, with 95% confidence intervals for all correlation analyses. Any significant association between the prevalence of serological markers and NATs was explored without bias.
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