Participant characteristics will be summarised using mean and SD or median and IQR for continuous variables, and number and percentages for the categorical variables. The proportion of participants recruited and lost to follow-up at the end point will be estimated with 95% CIs. As part of the secondary analyses, logistic regression models will be used to estimate the intervention effects with 95% CIs for the prespecified outcomes, tobacco abstinence and self-reported motivation and intention to quit after adjusting for the stratification factor (UPHC) and baseline values of the outcome where it is available. These analyses will be done on an intention to treat (participants as randomised with available outcome data) basis, and multiple imputation will be used to handle missing outcome values if considered appropriate. The extent of missing data for each variable and the percentage of participants adhering to the intervention will be reported. Attrition levels by randomised group and the characteristics of participants who are lost to follow-up will also be reported. All analyses will be done using STATA software V.17 (StataCorp. 2019. Stata Statistical Software: Release V.17. College Station, Texas: StataCorp LLC) or updated versions. A detailed statistical analysis plan will be drawn up nearer to the analysis stage, prior to the database lock.
Stata software v 17
Manufactured by StataCorp
Sourced in United States
STATA software V.17.0 is a comprehensive, integrated statistical software package designed for data analysis, management, and presentation. It provides a wide range of tools and functions for statistical modeling, data visualization, and data management. The software is widely used in various fields, including social sciences, economics, and healthcare.
Automatically generated - may contain errors
17 protocols using stata software v 17
1
Smoking Cessation Intervention Efficacy
2
Embryo Transfer Outcomes Analysis
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All data were analyzed using SPSS® 25 (IBM, Armonk, NY, USA) except for the normality test for all variables, which was the Shapiro–Francia test, only available at STATA® software V. 17 (StataCorp LLC, Lakeway Drive, Texas, TX, USA). Variables showing a skewed distribution were reported as median and range instead of average and standard deviation as best measures of centrality and variability for skewed distributions; intergroup differences in these variables were assessed for significance using a Kruskal–Wallis test or Mann–Whitney U test for independent samples. Potential pairwise relationships between donor characteristics and metabolic traits and embryo transfer outcomes were explored using Pearson correlation.
Logistic regression was used to assess effects of different factors on the probability of pregnancy after embryo transfer. In this process, stepwise forward modeling based on the Wald statistic criterion of p > 0.10 was used. The model included embryo type, embryo origin, embryo quality, recipient breed, body condition score (BCS), parity, open days, heat stress at embryo transfer, suckling status at embryo transfer, as well as pairwise interactions between these effects. Recipient and farm were included as fixed factors.
For all analyses, statistical significance was defined at a 5% threshold (p < 0.05), while tendencies were defined at p < 0.10.
Logistic regression was used to assess effects of different factors on the probability of pregnancy after embryo transfer. In this process, stepwise forward modeling based on the Wald statistic criterion of p > 0.10 was used. The model included embryo type, embryo origin, embryo quality, recipient breed, body condition score (BCS), parity, open days, heat stress at embryo transfer, suckling status at embryo transfer, as well as pairwise interactions between these effects. Recipient and farm were included as fixed factors.
For all analyses, statistical significance was defined at a 5% threshold (p < 0.05), while tendencies were defined at p < 0.10.
3
Antibiotic Use Patterns and Prevalence
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Data were reviewed by members of the research team and were entered into the online WHO PPS platform. The Data set was exported from the online WHO PPS platform to the Stata software V.17 (StataCorp, Texas, USA) which we used for the data cleaning and analysis. We used the χ2 test to check for an association between antibiotic use and age and sex. Categorical data were reported as frequencies with percentages while continuous data were presented as medians (with IQRs). Prevalence of antibiotic use was defined as the number of patients receiving at least one antibiotic on the day of the survey divided by the total number of patients on admission at the time of the survey. Prevalence was presented as percentages with a 95% CI.
4
Cardiovascular Health Factors in Benin
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The distribution of the participants’ characteristics was summarised using descriptive statistics. Sampling weights were used in the analyses, and the results are presented as proportions. The prevalence of CVH metrics was standardised according to the age and sex structure of the adult population provided by the 2013 census in Benin. Crude and multivariable modified Poisson regression models were used to identify factors associated with meeting 6–7 CVH metrics. A p<0.05 was considered statistically significant. All data were analysed using STATA software V.17.0 (StataCorp).
5
Glycemic and Lipid Profiles Across Diabetes Status
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Descriptive statistics were produced for all participants. Dependence of continuous variables (e.g. HbA1c, plasma lipids) on the independent variables of time (baseline, Time 1 and Time 2), sex (F/M), and diabetes status (healthy, prediabetes, type 2 diabetes) was assessed using mixed model analysis (xtmixed command, Stata software v. 17.0, StataCorp LLC, College Station, TX). Ordinal dependent variables were assessed versus time (baseline, Time 1 and Time 2), sex, and diabetes status using ordinal logistic regression analysis (ologistic command, Stata). Binary dependent variables were assessed using logistic regression (logistic command, Stata). A P value of < 0.05 was considered significant.
6
Retrospective Analysis of Oncological Outcomes
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Statistical analysis was performed in March 2022 using Stata software, v17.0 (StataCorp); a statistically significant difference was concluded when p<0.05. Categorical variables were analyzed using the chi-square or Fisher exact test. Continuous variables were analyzed using the Student’s t-test. The Kaplan–Meier method was used to estimate survival function, and the log-rank test was used to compare survival functions. The univariate Cox proportional hazard regression model was also used to examine the correlation of clinically relevant covariates that were likely to affect oncological outcomes. These included patient age, tumor grade, hormonal profile, pathological tumor stage, nodal disease, and adjuvant therapy received. A multivariate analysis was performed with variables with significant p-values in the univariate model.
The study was approved by the Institutional Review Board of Kyungpook National University (2015-05-205) and conducted in compliance with the principles of the Declaration of Helsinki.
The study was approved by the Institutional Review Board of Kyungpook National University (2015-05-205) and conducted in compliance with the principles of the Declaration of Helsinki.
7
Factors Influencing Early Antenatal Care
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Data from the Kobo server were exported to an Excel worksheet (Microsoft Corporation), cleaned and coded. Coded datasets from Excel spreadsheets (CSV) were imported to STATA software V.17.0 and analysed at three levels. Univariable analysis was used to describe variables independently. Continuous variables were reported as means and SDs, while categorical variables were reported as percentages and frequencies. At the bivariate level, associations between first ANC attendance within 12 weeks of pregnancy (the dependent variable) and the independent variables were tested using univariate logistic regression at p=0.2. A binary logistic regression analysis was run to estimate the net effect of each independent factor on the dependent variable at p=0.05 using the backward elimination method while testing the necessary assumptions first. Finally, the Hosmer-Lemeshow goodness of fit was run, and a p value of 0.854 was reported, indicating that the model fits the data.
8
Quantitative Analysis of Athlete Fitness
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Descriptive analysis was carried out using means and standard deviation (±SD) or median and interquartile range (IQR) for the quantitative variables and absolute and percentage values for the qualitative ones. The Shapiro–Wilk test was applied to assess normality. Univariate comparisons (athletes vs. ctr) were investigated using a non-parametric Wilcoxon rank-sum test for quantitative data. Only for the group of athletes, the non-parametric Wilcoxon matched-pairs signed-rank test was used to compare the differences in the two times considered (0 months and 2 months). Statistical significance was taken at the <0.05 level. All analyses were performed using Stata software v17.0 (StataCorp, College Station, TX 77845, USA).
9
Statistical Analysis of Participant Characteristics
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Descriptive statistics were calculated for therapist and patient characteristics. A difference of p<0.05 was considered statistically significant. Data were analysed using IBM SPSS Statistics for Windows V.28.0 and STATA software V.17.0.
10
Meta-analysis of Genetic Associations
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Meta-analysis based on the inverse-variance method was performed using the Genome-Wide Association Meta-Analysis (GWAMA) software (http://www.well.ox.ac.uk/gwama/ ) [25 (link)]. Overall estimates were calculated under the fixed- or randomeffect estimations. The Cochran Q test was performed for estimation of I2 statistics to detect potential publication bias across individual studies. Funnel plots were illustrated for the visualization of a potential publication bias. Forest plots were drawn to describe effect size of each study and overall outcome. The plots were generated using the STATA software v.17.0 (Stata Corp., College Station, TX, USA).
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