Data typing and processing were performed on the Statistic Package for Social Sciences—SPSS, version 22.0 (IBM Corporation, Nova York, NY, USA) for Windows. Data analysis were made on STATA for Windows, version 14.0 (StataCorp, College Station, TX, USA), in the Laboratory for Teaching, Research and Extension in Collective Health (LEPESC) of the State University of Bahia (UNEB), Brazil.
Stata version 14 for windows
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Stata version 14 for Windows is a statistical software package designed for data analysis, management, and visualization. It provides a comprehensive set of tools for researchers, analysts, and statisticians to conduct a wide range of statistical analyses, including linear and nonlinear regression, time series analysis, and multivariate techniques. Stata version 14 for Windows is available for the Windows operating system.
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Lab products found in correlation
28 protocols using stata version 14 for windows
1
Data Analysis in Social Sciences
2
Predictors of Transition Training Perception
3
Serum FT3 Levels Predict Outcome in Patients
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The categorical variables were expressed as counts (%), whereas the continuous variables were expressed as the mean (standard deviation, SD) or median (interquartile range [IQR]) values as appropriate. The age, immunotherapy delay, hospital stay, WBC and the concentrations of CRP were log-transformed to approximate a normal distribution. The univariate logistic regression was used to identify factors significantly associated with an increased risk of poor outcome; for each variable, the odds ratio (OR), and 95% confidence interval (CI) were given. Relevant variables with P < 0.05 in the univariate logistic regression analysis were entered into multivariate logistic regression models to identify whether variables were independently associated with outcome.
Patients were equally divided into 3 subgroups based on their serum FT3 levels. Baseline demographic and clinical features were compared across the three subgroups using the one-way analysis of variance (ANOVA), the equality-of-medians test or the Fisher’s exact test as appropriate. For differences within subgroups, the pair wise comparison with Bonferroni correction was used. All analyses were performed using Stata for Windows, Version 14.0 (StataCorp LLC., USA). P value < 0.05 was considered to be statistically significant.
Patients were equally divided into 3 subgroups based on their serum FT3 levels. Baseline demographic and clinical features were compared across the three subgroups using the one-way analysis of variance (ANOVA), the equality-of-medians test or the Fisher’s exact test as appropriate. For differences within subgroups, the pair wise comparison with Bonferroni correction was used. All analyses were performed using Stata for Windows, Version 14.0 (StataCorp LLC., USA). P value < 0.05 was considered to be statistically significant.
4
Survival Analysis of Systemic Sclerosis-Cardiac Manifestations
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The descriptive data are presented as frequency (%), mean ± SD, or median (interquartile range 1,3 [IQR 1,3]). Chi-square or Fisher’s exact test was used to compare the categorical variables between SSc-CM and those with non-CM. Student’s t-test or Mann–Whitney U test was used to compare the continuous variables between the two subgroups. The data were censored when any of the following events occurred: SSc-CM, reached the end of the study or death. The cumulative survival from the study entry was analysed using The Kaplan–Meier method. The survival between the two subgroups was compared using Log-rank test.
Multivariate Cox regression analysis with forward-stepwise selection, in which the probability of entering variables into the model was 0.15 and the probability of removing from the model was 0.2, was used to define the predictor for the evolution of SSc-CM. The variance inflation factor (VIF) and tolerance of our predictor variables were checked. p values < 0.05 were considered statistically significant. Statistical analyses were performed using Stata for Windows version 14.0 (TX, USA).
Multivariate Cox regression analysis with forward-stepwise selection, in which the probability of entering variables into the model was 0.15 and the probability of removing from the model was 0.2, was used to define the predictor for the evolution of SSc-CM. The variance inflation factor (VIF) and tolerance of our predictor variables were checked. p values < 0.05 were considered statistically significant. Statistical analyses were performed using Stata for Windows version 14.0 (TX, USA).
5
Marijuana Use, Oral HPV, and Periodontitis
6
Pregnancy Intention and Late ANC Initiation
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The necessary information was extracted from each original study by using a format prepared in a Microsoft Excel spreadsheet. Then the data were exported to STATA for Windows version 14 and used to calculate the pooled effect size with 95% confidence intervals of pregnancy intention on late initiation of ANC using the DerSimonian and Laird random effects meta-analysis (random effects model). The logarithm and standard error of the odds ratio (OR) for each included study were generated using “generate” command in STATA. Meta-regression was conducted to identify the source of heterogeneity, and statistically significant results were declared in the presence of heterogeneity.
7
Predicting Risk of Clinically Relevant Complications
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Data were expressed in median and IQR, when appropriate. The Mann–Whitney test was used for comparison of continuous variables, whereas the Chi-squared test or Fisher’s exact test were used for comparisons of categorical variables. Univariate logistic regression analysis was performed to predict the risk of developing a CCI ≥ 29.6. After evaluation of multicollinearity, multivariate logistic regression analysis was carried out on the variables, which reached p < 0.1 at univariate analysis. Odd ratios (OR) were adjusted for clustering on each center. All statistical tests were two-tailed, and differences were considered significant at a p-value of ≤0.05. Data analysis was performed with STATA for Windows (version 14).
8
Epidemiological Trends of Herpes Zoster
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For the annual crude C.incidences and H.incidences we estimated Poisson 95%CIs. Annual age-standardized C.incidences and H.incidences were calculated per 100,000 of the total European Standard population (ESP) 2013 taking into account the age groups according to the ESP.49 For trend analyses, we derived the incidence rate ratio (IRR) with 95%CI by using negative binomial regression. We conducted trend analyses for C.incidences with monthly HZ cases as dependent variable, year as continuous independent variable, and population size as the exposure variable. Trend analyses for H.incidences were conducted with annual hospitalized HZ cases as dependent variable, year as continuous independent variable, and population size as the exposure variable. A p-value <0.05 was considered as statistically significant.
All analyses were performed with Stata for Windows, version 14 (StataCorp, College Station, TX).
All analyses were performed with Stata for Windows, version 14 (StataCorp, College Station, TX).
9
Predicting Personal UVR Exposure
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Nonparametric tests were used to generate summary statistics. Univariate regression models were developed using the logtransformed measured personal UVR dose as the outcome and each of ambient UVR levels, self-reported time spent outdoors and modelled personal UVR exposure as the predictor. We log-transformed the outcome variable (measured personal UVR exposure) in order to obtain approximate normality and linearity. The coefficient of determination (R 2 ) was used to assess the variation in the measured personal UVR exposure explained by each surrogate. We then stratified the regression analyses based on occupation type (indoor vs. outdoor workers) and season (winter vs. summer). The season variable was constructed by grouping July, August and September as the summer months and October, November, December and January as the winter months. Stata for windows version 14 was used for conducting all analyses (StataCorp, College Station, TX, USA). A P-value of less than 0Á05 was considered as statistically significant.
10
Depression's Impact on Educational and Occupational Outcomes
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Multiple logistic regression analyses were used to model the association of depression symptoms reported at wave one, two, or both waves on: (1) completion of compulsory secondary education; (2) employment status at wave three; and (3) perceived exposure to psychological job demands, skill discretion, decision authority, job control, job strain, and workplace incivility at wave three.
Two models were presented for each outcome: (1) unadjusted associations; and (2) following adjustment for covariates. All analyses were undertaken in Stata for Windows, version 14 [26 ].
Two models were presented for each outcome: (1) unadjusted associations; and (2) following adjustment for covariates. All analyses were undertaken in Stata for Windows, version 14 [26 ].
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