All analyses were conducted using Stata SE 15.1. Factors with one or two measures were analysed as observed in the structural models, where indices are based on items with equal weight (Newsom, 2015 ). Factors with three or more items were analysed as latent factors, after testing for longitudinal metric invariance.
Se 15
Manufactured by StataCorp
The SE 15.1 is a laboratory equipment product offered by StataCorp. It serves as a core function, but a detailed description while maintaining an unbiased and factual approach is not available.
Automatically generated - may contain errors
Lab products found in correlation
17 protocols using se 15
1
Structural Equation Modeling in Stata
2
Pancreatic Cancer RNA-seq Analysis Protocol
3
SNHG15 Prognostic Meta-Analysis
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Statistical analysis was performed using RevMan 5.3.3 software and Stata SE 15.1 software. HR for death or recurrence and 95% CI were calculated between the high and low SNHG15 expression group. Patients with HR ≥ 1 indicated a poor prognosis. Furthermore, the correlation between SNHG15 expression and clinicopathological characteristics was evaluated using OR and 95% CI. Heterogeneity between the included studies was determined by the I2 value from the Cochrane Q test and the P value from the chi-square test. If there was heterogeneity (I2 ≥ 50% or P < 0.05), the results were summarized using a random-effects model. Instead, fixed-effects models were used for analysis. Otherwise, a random-effects model was applied. Begg's funnel plot and Egger's test were used to assess publication bias. A sensitivity analysis was applied to assess the stability of the results. P ≤ 0.05 was considered statistically significant.
4
Biomarker Profiling in Myotonic Dystrophy
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Descriptive results are expressed as means ± SD as well as the median and interval between the 1st and 4th quartile. Although this study is exploratory and primarily hypothesis generating, we performed preliminary analyses to compare biomarker profiles between CI vs. CU DM1 participants and to investigate associations between biomarker modalities. First, we performed uncorrected t tests on CSF and plasma biomarker levels between CI and CU participants. Second, we examined correlations between CSF and plasma biomarkers through Spearman rank correlation matrix using Graphpad Prism Software 8.4.2; correlations are represented on a heatmap. Spearman correlations between CSF and plasma biomarker levels were also explored (Stata SE 15.1). P values < 0.05 were considered significant.
5
Statistical Analysis of SIRS and SYND1 Levels
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Data were analysed in Stata SE 15.1. Histograms were visually inspected for Gaussian distribution and the Shapiro-Wilk test was performed to evaluate data for normal distribution prior to statistical analysis. An ANOVA and linear regression analysis were used for the analysis for normally distributed data. Regarding SIRS scores, categorical data across the three groups were analysed using the Chi-Square test for independent outcomes. A Mann Whitney U test was subsequently performed to evaluate for significance regarding pairwise comparisons between the groups (data presented as median values with range). An unpaired, nonparametric t-test (Mann-Whitney) was used to determine whether SYND1 levels differed between survivors and non-survivors. A Chi-square test of independence was 20423306, 2023, 3, Downloaded from https://beva.onlinelibrary.wiley.com/doi/10.1111/evj.13862, Wiley Online Library on [28/08/2024]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License performed to examine the relation between gender and group assignment. Unless noted otherwise, data are reported as mean ± standard deviation. Significance was set at P ≤ 0.05.
6
Meta-analysis Protocol Utilizing Stata
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
In this meta-analysis, the Stata SE 15.1 version was used. Each value of the results is represented by a 95 % confidence interval (95 %CI) in a random effects model. The heterogeneity between studies was assessed by the I2 (%) test. In Stata 12.0 software, Egger and Begg tests were used to find publication bias. P < 0.05 was considered to be a significant publication bias. And a sensitivity analysis was performed to test result stability.
7
Malnutrition Severity and Treatment Outcomes
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Data analysis was conducted using Stata SE15.1.
Since we hypothesized that nutrition outcomes and resource use were likely to be correlated with disease severity and duration of treatment, as a proxy for malnutrition severity we divided patients into four categories according to their MUAC at treatment initiation: 120 ≤ MUAC < 125, 115 ≤ MUAC < 120, 110 ≤ MUAC < 115, MUAC < 110.
The cumulative proportion of children who recovered, defaulted or died by a certain time in treatment was calculated according to the initial MUAC status. For each category, we examined the average duration of treatment, the number of clinic visits, inpatient days, RUTF sachets and medications used. Total treatment cost in the study was calculated by multiplying the quantity of each resource used by its unit cost and summing across resource categories.
Since we hypothesized that nutrition outcomes and resource use were likely to be correlated with disease severity and duration of treatment, as a proxy for malnutrition severity we divided patients into four categories according to their MUAC at treatment initiation: 120 ≤ MUAC < 125, 115 ≤ MUAC < 120, 110 ≤ MUAC < 115, MUAC < 110.
The cumulative proportion of children who recovered, defaulted or died by a certain time in treatment was calculated according to the initial MUAC status. For each category, we examined the average duration of treatment, the number of clinic visits, inpatient days, RUTF sachets and medications used. Total treatment cost in the study was calculated by multiplying the quantity of each resource used by its unit cost and summing across resource categories.
8
Analyzing Nicotine Concentration Trends
9
Assessing Missing Data in Intervention Study
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Missing data was primarily attributable to non-participation in follow-up assessments.
Participants who did not participate in one follow-up assessment were allowed to participate in all future follow-up assessments. Of the 560 women at baseline, 86% completed follow-up assessments at 3-month, 82% at 6-month, 81% at 9-month, and 83% at 12-month follow-up, respectively, comparable retention to previous interventions 19, 27 . To assess whether participants who attended each follow-up assessment differed from those who did not participate on 34 continuous and categorical covariates, Kruskal-Wallis tests were used for continuous variables and Pearson chi-squared tests for categorical variables. Of the 136 comparisons, 5 comparisons were significant at p ≤ 0.05 and 11 comparisons were p ≤ 0.1, within the range expected by chance, which is consistent with the data being missing completely at random (Supplementary Table 1). Despite lack of association of data missingness with observed data, a sensitivity analysis was conducted for the contingency that data were missing at random by repeating the analysis after multiple imputation with 10 imputations using Stata SE 15.1 (Appendix 1).
Participants who did not participate in one follow-up assessment were allowed to participate in all future follow-up assessments. Of the 560 women at baseline, 86% completed follow-up assessments at 3-month, 82% at 6-month, 81% at 9-month, and 83% at 12-month follow-up, respectively, comparable retention to previous interventions 19, 27 . To assess whether participants who attended each follow-up assessment differed from those who did not participate on 34 continuous and categorical covariates, Kruskal-Wallis tests were used for continuous variables and Pearson chi-squared tests for categorical variables. Of the 136 comparisons, 5 comparisons were significant at p ≤ 0.05 and 11 comparisons were p ≤ 0.1, within the range expected by chance, which is consistent with the data being missing completely at random (Supplementary Table 1). Despite lack of association of data missingness with observed data, a sensitivity analysis was conducted for the contingency that data were missing at random by repeating the analysis after multiple imputation with 10 imputations using Stata SE 15.1 (Appendix 1).
10
Statistical Analysis of Continuous and Categorical Variables
Check if the same lab product or an alternative is used in the 5 most similar protocols
+ Expand
Statistical analysis was performed using Stata SE15.0. Continuous variables are presented as mean ± standard deviation if normally distributed and as median M (first quartile Q1, third quartile Q3) if not normally distributed. Continuous variables were compared using a t-test and chi-square test if they conformed to normality; if not, the rank-sum test was used. A chi-square or Fisher’s exact probability test was performed to compare categorical variables. Statistical significance was set at p < 0.05.
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!