Intention-to-treat analysis was applied. Descriptive and inferential statistics were conducted (SPSSv.23; IBM®, Armonk, NY, USA) to compare the differences in clinical outcomes between the intervention (i.e., peak–trough-based vancomycin TDM approach) and the control (i.e., trough-only-based vancomycin TDM approach) arms. Skewness test was applied to ensure normality of data (choice of parametric vs nonparametric tests). For comparison between the groups, Student’s t-test, Mann–Whitney U-test or chi-squared test was used as appropriate. All comparisons were carried out using an a priori significance level of 0.05 (two-sided tests). AUCs were calculated using the nonlinear mixed-effects population pharmacokinetics modeling approach (NONMEM v.7.3, ICON, USA) [49 ]. Classification and regression tree (CART) analysis was conducted using SPSS v.23 (IBM®; Armonk). AUC24/MIC, vancomycin cumulative doses, treatment duration, infected physiologic compartment, ethnicity, CLCR and TDM approach were tested against clinical effectiveness. To assess the predictive accuracy of the generated models, misclassification risk estimates with standard error were used [50 ].
Spss v23
Manufactured by IBM
Sourced in United States, United Kingdom, Japan, Belgium, Italy, Austria, Germany, Australia, France
SPSS v23 is a statistical software package developed by IBM. It is designed to analyze and manipulate data. The software provides a range of tools and functions for data management, statistical analysis, and reporting.
Automatically generated - may contain errors
Lab products found in correlation
Prism v7, by GraphPad (25 mentions)
Prism 8, by GraphPad (15 mentions)
Prism 7, by GraphPad (12 mentions)
Spss statistics for windows version 23, by IBM (11 mentions)
Prism v6, by GraphPad (10 mentions)
Prism 6, by GraphPad (10 mentions)
Spss statistics for window, by IBM (9 mentions)
Prism v5, by GraphPad (8 mentions)
Prism v8, by GraphPad (7 mentions)
Sas v9, by SAS Institute (7 mentions)
Prism v9, by GraphPad (6 mentions)
Prism 5, by GraphPad (5 mentions)
Prism 9, by GraphPad (5 mentions)
Attune cytometric software v 1, by Thermo Fisher Scientific (4 mentions)
Image processing toolbox 9, by MathWorks (3 mentions)
Matlab 9, by MathWorks (3 mentions)
Matlab, by MathWorks (3 mentions)
Stata v 15, by StataCorp (3 mentions)
Sas version 9, by SAS Institute (3 mentions)
Stata v 13, by StataCorp (3 mentions)
2 071 protocols using spss v23
1
Optimizing Vancomycin Dosing Regimen
2
Survival Reduction Analysis by ANOVA
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
One-way analysis of variance (ANOVA) with Duncan’s post-hoc test (SPSS v. 23, IBM, Chicago, IL, USA) were used for determining and comparing the log reduction of the survival between different sampling points. Our data in this study proved not to violate the assumption of ANOVA, including normally distributed, independence of cases, and homogeneity of variance. The correlation between FI and log reduction of the survival was analyzed with Pearson correlation analysis (SPSS v. 23, IBM, Chicago, IL, USA). Differences between means with p-values lower than 0.05 (p < 0.05) were regarded as statistically significant. Otherwise, p-values lower than 0.05 (p < 0.05) were regarded as statistically correlated.
3
Normalized Inhibition Assay Protocol
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Normalized inhibition data were generated by applying the formula:
where sample is the raw value obtained from the readout of any well treated with an experimental compound, pos is the mean readout of all the positive control-treated wells from the same plate and neg is the mean readout of all the negative control-treated wells from the same plate.
The performance and reproducibility of the assays were monitored by measuring the %CV and Z′ of the assays53 (link), based on the negative and positive controls in each individual plate.
Normalized % inhibitions were plotted against log-transformed μM concentration of each compound and IC50 values were calculated using a 4 parameter non-linear regression analysis in GraphPad Prism v. 5.0, using the constraints bottom = 0 and top ≤ 100.
The correlation between the rank assigned to the GCS compounds and the hitting frequency of the compounds in our assays was assessed using a Kendall’s Tau-b algorithm in SPSS v.23 (IBM).
The comparison of the agreement between compounds activity at multiple HTS assay conditions and transmission blocking activity in SMFA was evaluated using the non-parametric Cochran’s Q test for related samples with pairwise comparisons, in SPSS v.23 (IBM).
where sample is the raw value obtained from the readout of any well treated with an experimental compound, pos is the mean readout of all the positive control-treated wells from the same plate and neg is the mean readout of all the negative control-treated wells from the same plate.
The performance and reproducibility of the assays were monitored by measuring the %CV and Z′ of the assays53 (link), based on the negative and positive controls in each individual plate.
Normalized % inhibitions were plotted against log-transformed μM concentration of each compound and IC50 values were calculated using a 4 parameter non-linear regression analysis in GraphPad Prism v. 5.0, using the constraints bottom = 0 and top ≤ 100.
The correlation between the rank assigned to the GCS compounds and the hitting frequency of the compounds in our assays was assessed using a Kendall’s Tau-b algorithm in SPSS v.23 (IBM).
The comparison of the agreement between compounds activity at multiple HTS assay conditions and transmission blocking activity in SMFA was evaluated using the non-parametric Cochran’s Q test for related samples with pairwise comparisons, in SPSS v.23 (IBM).
4
Neuropsychological Correlates of Brain Oscillations
5
Assessing Healthcare Access Barriers
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Filled questionnaires were checked for completeness, assigned a unique identifier and entered in Epi Data. The summary data set was then exported to SPSS V.23 for analysis. In preliminary analysis, we determined the reliability of the items in each of the four subsections of the questionnaire separately using the Cronbach’s alpha reliability coefficient
Next, we conducted dimension reduction using EFA in SPSS V.23 to generate fewer and meaningful (latent) variables with eigenvalues of 1 and above. To retain variables that clearly discriminate between the generated latent variables, the minimum cut-off for item loading was set at 0.7.
Finally, a regression was performed to determine the independent association of each of the generated latent variables to the dependent variable—‘perceived ease of access to healthcare’ on the opposite side of the border. Beta coefficient, CI and p values were used to gauge the strength of association of latent factors with the dependent variable. Results were stratified by case condition and by dimension of the health access framework.
Next, we conducted dimension reduction using EFA in SPSS V.23 to generate fewer and meaningful (latent) variables with eigenvalues of 1 and above. To retain variables that clearly discriminate between the generated latent variables, the minimum cut-off for item loading was set at 0.7.
Finally, a regression was performed to determine the independent association of each of the generated latent variables to the dependent variable—‘perceived ease of access to healthcare’ on the opposite side of the border. Beta coefficient, CI and p values were used to gauge the strength of association of latent factors with the dependent variable. Results were stratified by case condition and by dimension of the health access framework.
6
Genetic Markers and Depression Profiling
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
To calculate the genotype and allele frequencies of all examined SNPs and to manage the HWE test, we used PLINK v.1.9 [42 (link)]. A measure of linkage disequilibrium between SNPs located in the same genetic locus was assessed via r2 (PLINK v.1.9). The Kolmogorov–Smirnov’s test was used (SPSS v.23, SPSS Inc., Chicago, IL, USA) to examine the correspondence of BDI-measured depression score to the normality of distribution. The effect of social–demographic categorical variables on individual variance in depression level was evaluated via the Mann–Whitney U test (SPSS v.23). For this analysis, we applied a correction for multiple testing for the number of examined social–demographic parameters (PFDR < 0.05/8), which provided us with a threshold of PFDR < 0.0063.
To estimate the main effects of genetic variants on depression levels and to obtain standardized regression coefficients for each SNP, a series of linear regression analyses was carried out adjusted for sex, age, and ethnicity in the total sample under the additive effect of SNPs in PLINK v.1.9. Obtained regression coefficients served as effect estimates for the subsequent examination of the association of individual polygenic scores and depression level in young adults under linear regression in R v.4.1.2 [43 ].
To estimate the main effects of genetic variants on depression levels and to obtain standardized regression coefficients for each SNP, a series of linear regression analyses was carried out adjusted for sex, age, and ethnicity in the total sample under the additive effect of SNPs in PLINK v.1.9. Obtained regression coefficients served as effect estimates for the subsequent examination of the association of individual polygenic scores and depression level in young adults under linear regression in R v.4.1.2 [43 ].
7
Cyber Victimization and Technology Addiction
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
The data analyses were performed using SPSS v23 and the PROCESS macro [65 ]. First, all missing items values were imputed using the expectation-maximization (EM) method with SPSS v23. Later, descriptive statistics, internal consistency, and Pearson correlation analysis among the study variables were calculated. We then examined the interaction between cyber victimization and core self-evaluations for problematic use of each of the new technologies (i.e., Internet, smartphones, and social media). We examined the moderating aspects using Hayes’ PROCESS macro (Model 1) [65 ].
8
Tailoring Dietary Advice via Psychological Profiles
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
A Principal Component Analysis (PCA) was conducted on the 68 items in the survey (using SPSS v.23), which resulted in a four dimensional solution on which a Varimax rotation was performed. The scores for this four dimensional model are used to predict reported preferences for three different forms in which dietary health information can be provided to people. These predictions have been carried out separately by means of three logistic regression analyses (using SPSS v.23). The reason to carry out three separate logistic regressions, rather than combining these into one model in order to include interactions between dependent variables is rather practical. One of the aims of this study was to explore the possibility to use the psychological profiles obtained to provide tailored dietary advice in a specialized computer program (ultimately in a smartphone-app). The three dependent variables are linked to matters that could be separately implemented in such an app. Furthermore, in hindsight, the correlations between the three variables turned out to be very low (Supplementary Material ).
9
Validating Research Instrument Reliability
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
A factor analysis was conducted using SPSS V.23 to validate that our indexes contained relevantly grouped individual items. The factors were first extracted using direct oblimin rotation. The Kaiser-Meyer-Olkin Measure of Sampling Adequacy was 0.75, indicating that a factor analysis was appropriate for the material, while Bartlett's Test of Sphericity had a significance of 0.000, indicating that the data were appropriate for factor analysis. The highest correlation between our factors was 0.35, validating the use of the direct oblimin rotation. The scree plot suggested using four factors, but performing the exploratory factor analysis to validate our five indexes, we chose to extract five factors. The factor analysis generally validated our scales as seen in table 1 . The extracted factors had a high degree of correspondence with those constructed on theoretical bases a priori. As a final test, we calculated the Cronbach α (using SPSS, V.23) on our indexes, identifying a range from 0.73 for ‘Data Quality and Usefulness’ to 0.91 for ‘Management Request of Registry Data’. Details are found in table 1 and online supplementary file II .
10
Dairy Cow Dietary Cation-Anion Difference
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
All data were analyzed by ANOVA and results for the pre-and postpartum periods were analyzed separately with SPSS V23
(https://www.ibm.com/eg-en/analytics/spss-statistics-software). Data were analyzed by mixed models using the MIXED procedure of SPSS V23. Responses with a single measurement per cow were analyzed with the xed effects of prepartum level of DCAD (0, -120 or -200 mEq/kg DM), postpartum level of DCAD (+ 200 and + 400 mEq/kg DM), day ( -21, -14, -7, -2, 0, 2, 7, 14 and 21) and the interaction of prepartum or postpartum DCAD. Data with repeated measures within the experimental design were analyzed with the same mixed model described above and included the xed effects of day and the interactions prepartum DCAD x day, postpartum DCAD x day, prepartum DCAD x postpartum DCAD x day. Cow nested within prepartum DCAD and postpartum DCAD was a random effect in the model and accounted for. The repeated statement was included in all mixed models with repeated measurements with day speci ed as the repeated effect. Data from the preliminary period were included as covariates in the analysis of prepartum and postpartum effects. Signi cance was declared at P ≤ 0.05, with a trend for variables with P > 0.05 and ≤ 0.10. Duncan's test was used to compare the differences among means.
(https://www.ibm.com/eg-en/analytics/spss-statistics-software). Data were analyzed by mixed models using the MIXED procedure of SPSS V23. Responses with a single measurement per cow were analyzed with the xed effects of prepartum level of DCAD (0, -120 or -200 mEq/kg DM), postpartum level of DCAD (+ 200 and + 400 mEq/kg DM), day ( -21, -14, -7, -2, 0, 2, 7, 14 and 21) and the interaction of prepartum or postpartum DCAD. Data with repeated measures within the experimental design were analyzed with the same mixed model described above and included the xed effects of day and the interactions prepartum DCAD x day, postpartum DCAD x day, prepartum DCAD x postpartum DCAD x day. Cow nested within prepartum DCAD and postpartum DCAD was a random effect in the model and accounted for. The repeated statement was included in all mixed models with repeated measurements with day speci ed as the repeated effect. Data from the preliminary period were included as covariates in the analysis of prepartum and postpartum effects. Signi cance was declared at P ≤ 0.05, with a trend for variables with P > 0.05 and ≤ 0.10. Duncan's test was used to compare the differences among means.
About PubCompare
Our mission is to provide scientists with the largest repository of trustworthy protocols and intelligent analytical tools, thereby offering them extensive information to design robust protocols aimed at minimizing the risk of failures.
We believe that the most crucial aspect is to grant scientists access to a wide range of reliable sources and new useful tools that surpass human capabilities.
However, we trust in allowing scientists to determine how to construct their own protocols based on this information, as they are the experts in their field.
Ready to get started?
Sign up for free.
Registration takes 20 seconds.
Available from any computer
No download required
Revolutionizing how scientists
search and build protocols!