To examine whether transparency in reporting improved over time, we compared the degree of key information reported in 2015 to that in 2013 in the two journals combined. To assess whether the presence of a checklist had an impact, we compared the changes over time observed in Nature with those observed in Cell. The flow of the analysis is shown in Fig 1B . Given the sample size and the categorical variables, Fisher exact test was performed for our analyses. We did not correct for multiple comparisons because the hypotheses are independent and sample size was relatively small. All statistical analyses were two-sided, and performed using Stata Statistical Software: Release 14.1 (StataCorp. 2015. College Station, TX: StataCorp LP).
Stata statistical software release 14
Manufactured by StataCorp
Sourced in United States
Stata Statistical Software: Release 14 is a comprehensive, integrated statistical software package developed by StataCorp. It provides a wide range of data management, statistical analysis, and visualization tools to support research and data analysis across various fields. The software is designed to be user-friendly and offers a flexible interface for both command-line and graphical user interactions.
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Lab products found in correlation
Stata 14, by StataCorp (25 mentions)
Stata version 14, by StataCorp (18 mentions)
Stata, by StataCorp (14 mentions)
Stata v14, by StataCorp (6 mentions)
Stata 14 statistical software, by StataCorp (4 mentions)
Stata software, by StataCorp (3 mentions)
Stata mp, by StataCorp (2 mentions)
Stata ic, by StataCorp (2 mentions)
Stata ic 14, by StataCorp (2 mentions)
Stata software version 14, by StataCorp (2 mentions)
Prism 8, by GraphPad (2 mentions)
Spss version 20, by IBM (2 mentions)
Sas 9, by SAS Institute (2 mentions)
Stata 15, by StataCorp (2 mentions)
Revman 5, by StataCorp (1 mentions)
Stata 12, by StataCorp (1 mentions)
Stata statistical software version 14, by StataCorp (1 mentions)
Spss 25, by IBM (1 mentions)
Stata software version 15, by StataCorp (1 mentions)
Stata se version 14, by StataCorp (1 mentions)
222 protocols using stata statistical software release 14
1
Reporting Transparency in Scientific Journals
2
Splenomegaly Diagnosis Accuracy Assessment
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The positive predictive value for the splenomegaly diagnosis was calculated with corresponding 95% confidence interval (CI). The STATA command diagt (STB-56: sbe36; STB-59: sbe36.1) was used to calculate PPV by dividing the number of patients with a valid diagnosis after review by the number of all patients. Descriptive tables were derived to illustrate the characteristics of the population in general and to compare differences across disease groups. P-values were calculated by using the chi-square test and the two-sample t-test. Data analyses were performed in STATA (STATACorp. 2015. STATA Statistical Software: Release 14.1 College Station, TX: STATACorp LP).
3
Correlation and Agreement of Standard and Tablet-based Tests
4
Elucidating Factors Affecting EGS Outcomes
5
Patterns of Primary Health Care Utilization among HIV-Positive Residents
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The main outcome for this analysis was the proportion of DSA residents visiting PHCs between April 2017 and March 2018, stratified by sex, age and HIV status.
We categorized PHC visits into three subgroups: 1. HIV visits, including antiretroviral therapy (ART) start or follow-up; 2. acute visits, including family planning, minor ailments, maternity, reproductive health, circumcision, or emergency care; and 3. other chronic (non-HIV), including care for TB, diabetes or hypertension.
All data was analysed using Stata (StataCorp. 2017.
Stata Statistical Software: Release 14.1. College Station, TX: StataCorp LLC).
We categorized PHC visits into three subgroups: 1. HIV visits, including antiretroviral therapy (ART) start or follow-up; 2. acute visits, including family planning, minor ailments, maternity, reproductive health, circumcision, or emergency care; and 3. other chronic (non-HIV), including care for TB, diabetes or hypertension.
All data was analysed using Stata (StataCorp. 2017.
Stata Statistical Software: Release 14.1. College Station, TX: StataCorp LLC).
6
Oral Health Predictors of Speech and Tongue Position
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Data were analyzed using the STATA 14 statistical program (StataCorp, 2014. Stata Statistical Software: Release 14.1, College Station, TX: StataCorp LP). This study considered two outcomes: (1) speech distortion (absent/present) and (2) tongue position (normal/altered). It was performed a descriptive analysis of the demographic, socioeconomic and oral health measures of the sample, such as: age, gender, skin color, father and mother schooling, Angle’s classification of malocclusion (Class I, II or III), overjet (adequate, accentuated and anterior cross bite), overbite (adequate, deep bite and anterior open bite), posterior crossbite and respiratory mode, according to the distribution of outcome variables.
Unadjusted analyses were performed to provide a preliminary assessment of the association between predictor variables and outcomes. Poisson regression models with adjusted robust variance were used to evaluate the association among predictors variables in the prevalence of speech distortion and tongue position. The exploratory variables that presented a value of p≤0.20 in the univariate analysis were included in the multivariate model. Results are presented as prevalence ratio (PR) and respective 95% confidence interval (95% CI). A significance level of 0.05 was considered.
Unadjusted analyses were performed to provide a preliminary assessment of the association between predictor variables and outcomes. Poisson regression models with adjusted robust variance were used to evaluate the association among predictors variables in the prevalence of speech distortion and tongue position. The exploratory variables that presented a value of p≤0.20 in the univariate analysis were included in the multivariate model. Results are presented as prevalence ratio (PR) and respective 95% confidence interval (95% CI). A significance level of 0.05 was considered.
7
Exploring Pathways of Social Capital
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The descript ive a nalysis was performed using STATA 14 software (StataCorp. 2014. Stata Statistical Software: Release 14.1. College Station, TX: StataCorp LP). The differences between the followed individuals and the dropouts were assessed using the Chi-squared test.
Structural equation modeling (SEM) was performed using Mplus to test the pathways between demographic, socioeconomic, clinical characteristics, and social capital, using the covariates at baseline (T1) and outcome studied at follow-up (T2). Our theoretical model was based on a previous published study, which also explores social capital as an outcome. 29 (link) SEM consisted only of a structural model, which analyzed the magnitude and direction of the paths between variables. The goodness-of-fit was evaluated using the Root Mean Square Error of Approximation (RMSEA), the Comparative Fit Index (CFI), the Tucker-Lewis index (TLI), and the Standardized Root Mean Square Residual (SRMR). An RMSEA value < 0.05 and a CFI and TLI > 0.95 indicates an adequate fit, respectively. The SRMR indicates an adequate fit at values lower than 0.8. 30 Two models were tested, and modification indices (MI) were used to evaluate the quality of fit. MI values equal or above 0.40 were considered paths that were not significant, and were removed systematically.
Structural equation modeling (SEM) was performed using Mplus to test the pathways between demographic, socioeconomic, clinical characteristics, and social capital, using the covariates at baseline (T1) and outcome studied at follow-up (T2). Our theoretical model was based on a previous published study, which also explores social capital as an outcome. 29 (link) SEM consisted only of a structural model, which analyzed the magnitude and direction of the paths between variables. The goodness-of-fit was evaluated using the Root Mean Square Error of Approximation (RMSEA), the Comparative Fit Index (CFI), the Tucker-Lewis index (TLI), and the Standardized Root Mean Square Residual (SRMR). An RMSEA value < 0.05 and a CFI and TLI > 0.95 indicates an adequate fit, respectively. The SRMR indicates an adequate fit at values lower than 0.8. 30 Two models were tested, and modification indices (MI) were used to evaluate the quality of fit. MI values equal or above 0.40 were considered paths that were not significant, and were removed systematically.
8
COVID-19 Severity and Mortality Meta-Analysis
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Analyses were conducted using Stata Statistical Software, release 14.1 (StataCorp LLC, College Station, TX, United States). A random-effects model was used for determining pairwise meta-analyses. The results are reported as odds ratios (ORs) and 95% confidence intervals (CIs). Heterogeneity in each pairwise comparison was estimated using the I2 statistic. Publication bias was assessed using a funnel plot, and Egger’s tests were employed to assess the funnel plot asymmetry. Sensitivity analysis and subgroup analysis by level of PA were performed to evaluate the robustness of the results in determining the severity and mortality of illness in COVID-19 patients.
9
Comparison of Treatment Outcomes
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Data were reported as percentages for categorical variables and as means (95% confidence intervals) for quantitative variables. The comparison between groups was conducted using Fisher’s exact test for contingency tables and the z-test for the comparison of proportions. A non-parametric Mann–Whitney U test was used to compare quantitative variables between groups. The Wilcoxon signed-ranked test was used for post- versus baseline comparisons.
A two-tailed p < 0.05 was considered statistically significant. Stata Statistical Software: Release 14.1, College Station, TX, USA: StataCorp LP) was used for database management and analysis. The data analysis was conducted by a statistician blinded to the intervention and control groups.
A two-tailed p < 0.05 was considered statistically significant. Stata Statistical Software: Release 14.1, College Station, TX, USA: StataCorp LP) was used for database management and analysis. The data analysis was conducted by a statistician blinded to the intervention and control groups.
10
Factors Influencing COVID-19 Outcomes
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We used median and interquartile range (IQR) for continuous variables. Dichotomous variables were shown as frequency (percentage). We used multiple logistic regression to determine the contributing factor of ICU admission and COVID-19 case fatality rate (CFR). For the model generation, we first investigated the association between the baseline characteristics (including: age, sex, comorbidities, clinical signs, and symptoms) and the target outcomes (ICU admission and death) using simple logistic regression. We then used the Wald test to select the most influential variables (with P < 0.2) for the multiple logistic regression model. We provided each factor's odds ratio (OR) and 95% confidence interval (CI). All statistical analyses were performed using Stata software (StataCorp. 2014. Stata Statistical Software: Release 14.1, College Station, TX: StataCorp LP), and P-values less than 0.05 were considered significant.
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