Survival outcomes included the ocular salvage, overall survival, and recurrence-free survival rates estimated using the Kaplan-Meier method. Basic characteristics included patient factors (sex, age at diagnosis, and first signs), tumor factors (eye stage and laterality), and treatment factors (treatment group, additional treatments, and number of IAC cycles). Ocular and systemic complications were also recorded. All statistical analyses were performed using Stata 12.0 for Windows (Stata, Inc., Chicago, IL, USA). All tests were two-sided, and a P-value < 0.05 was considered statistically significant.
Stata 12.0 for windows
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STATA 12.0 for Windows is a comprehensive, integrated statistical software package that provides a wide range of data analysis, data management, and graphics capabilities. It is designed to handle a variety of data types and can be used for tasks such as data manipulation, statistical modeling, and visualization. STATA 12.0 for Windows is available for the Windows operating system and is a powerful tool for researchers, analysts, and professionals in various fields.
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Lab products found in correlation
17 protocols using stata 12.0 for windows
1
Intra-arterial Chemotherapy Survival Outcomes
2
Angiotensin Levels in Cardiovascular Disease
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Continuous variables were tested for normal distribution by Kolmogorov-Smirnov test and appropriately presented as mean (SD) or median (IQR, interquartile range). Categorical variables were shown as percentage. Comparisons between groups were performed by one-way ANOVA (or Kruskal-Wallis test), as appropriate, with Bonferroni post hoc test for intergroup comparisons. Comparison between categorical data were using the chi-square test or a Fisher's exact test. Correlations between two continuous variables were performed using the Pearson correlation coefficient, or Spearman rank correlation for nonnormally distributed data. Ordinal regression analysis was used to determine changes in ANG levels in relation to disease severity, where the pseudo R-square value was reported to explain the variation in ANG in response to the severity of disease. Receiver operator characteristic (ROC) curves were used to evaluate the performance of ANG, NT-proBNP, and LVEF, depicted by the mean area under the curve (AUC) with 95% CI. We treated p values < 0.05 as a statistically significant and used Stata 12.0 for Windows (StataCorp, TX, USA) to perform statistical analysis.
3
Genetic and Clinical Factors in AIAA
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We performed data analyses using STATA 12.0 for Windows (STATA Corporation, College Station, TX). Because genetic heterogeneity or population stratification has the potential to lead to either spurious association or reduced power, we carried out population-specific analysis and report here the results restricted to White subjects.
We tested associations between AIAA and demographic and clinical variables using the χ2 test. Demographic and clinical variables with P values of < .10 in the bivariate analyses were carried forward to the multivariable logistic regression models. Next, we examined the association between AIAA and genetic polymorphisms using the χ2 test. Genetic SNPs with P values of < .001 (Bonferroni Adjustment) in the bivariate analyses were carried forward to the multivariable logistic regression models. We performed logistic regression analyses in two steps: 1) Model 1 included only the demographic and clinical variables, and 2) Model 2 added the genetic SNPs to Model 1.
We tested associations between AIAA and demographic and clinical variables using the χ2 test. Demographic and clinical variables with P values of < .10 in the bivariate analyses were carried forward to the multivariable logistic regression models. Next, we examined the association between AIAA and genetic polymorphisms using the χ2 test. Genetic SNPs with P values of < .001 (Bonferroni Adjustment) in the bivariate analyses were carried forward to the multivariable logistic regression models. We performed logistic regression analyses in two steps: 1) Model 1 included only the demographic and clinical variables, and 2) Model 2 added the genetic SNPs to Model 1.
4
Evaluating Point-of-Care Testing Satisfaction
5
Equivalence of Shoulder Pain Management
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The primary outcome measure was the change in SPADI score between baseline and week 26. The equivalence margin was set at (−10, +10) as this is reported as a clinically significant change in Total SPADI score [20 (link)]. The 95% CI of mean difference (µ1−µ2) in Total SPADI score within the range of −10 to +10 is therefore considered as the proof for the equivalence of both interventions. The null hypothesis was that the treatments are not equivalent, i.e., (µ1−µ2) <= −10 or (µ1−µ2) >= 10. A two one sided test (TOST) was used to test the hypothesis. The number of patients with 26 weeks SPADI was low. A post-hoc analysis was carried out imputing the week 12 values for the patients who did not have 26 week data. This was not included in the initial plan.
Secondary outcomes were summarised using n, Mean (SD), and Median (IQR). The difference between the groups were compared using analysis of covariance, adjusting for the baseline values. Stata 12.0 for Windows (Stata Corporation, College Station, TX, USA) and R 3.0.2 (R Foundation for Statistical Computing. Vienna, Austria) was used for the data analysis.
Secondary outcomes were summarised using n, Mean (SD), and Median (IQR). The difference between the groups were compared using analysis of covariance, adjusting for the baseline values. Stata 12.0 for Windows (Stata Corporation, College Station, TX, USA) and R 3.0.2 (R Foundation for Statistical Computing. Vienna, Austria) was used for the data analysis.
6
Risk Factors for Chemotherapy-Induced Neuropathy
7
Hepatitis Prevalence and Factors
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We used STATA 12.0 for windows (STATA corp, college station, TX, USA) for data analysis. Participant’s characteristics were reported as count and percentages and mean and standard deviation, and compared them across major subgroups via Chi square tests and equivalents for qualitative variables, and Student’s t-test for continuous variables. For further analysis, we categorized some continuous variables without any clinical cut-off at their medians and described hepatitis as carrying any of the 2 markers (HBsAg and anti-HCV antibodies). Thus associated factors of hepatitis were analysed by univariable logistic regression reporting Odds ratios (OR) and their 95% confident intervals (CI). All significant variables were mutually introduced in a final multivariable logistic regression model. A p-value <0.05 was used to indicate statistically significant results.
8
Barriers to Acupuncture Utilization
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Research assistants entered all data with verification by a separate data
manager. Less than 5% of the data were missing in all of the key variables
described in the article. Data analysis was performed using STATA 12.0 for
Windows (STATA Corporation, College Station, TX). To identify the reasons
patients were hesitant to use acupuncture, if a participant answered “agree” or
“strongly agree,” we considered this is to be one of the factors associated with
barriers to use. We performed multiple linear regression analysis to determine
the relationship between relevant socioeconomic factors associated with
perceived barrier scores. In addition, we performed cross-tab analysis between
race and education. All analyses were 2-sided with a P less
than .05 indicating significance. Our sample size was determined by the parent
study.
manager. Less than 5% of the data were missing in all of the key variables
described in the article. Data analysis was performed using STATA 12.0 for
Windows (STATA Corporation, College Station, TX). To identify the reasons
patients were hesitant to use acupuncture, if a participant answered “agree” or
“strongly agree,” we considered this is to be one of the factors associated with
barriers to use. We performed multiple linear regression analysis to determine
the relationship between relevant socioeconomic factors associated with
perceived barrier scores. In addition, we performed cross-tab analysis between
race and education. All analyses were 2-sided with a P less
than .05 indicating significance. Our sample size was determined by the parent
study.
9
Adhesive Removal Surface Roughness Analysis
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Sample calculation was performed by G*Power 3.1.3 (Heinrich Heine Universität, Düsseldorf, Germany) with a power study of 80% and α = 0.05, totaling a minimum number of 18 specimens per group, in accordance with a previous pilot study. Data normality was checked applying the Shapiro-Wilk test for measures distribution. Within each group, differences between the initial and final surface roughness were analyzed, and the Wilcoxon signed rank test was used to pair the differences. A roughness analysis among groups and the time required for adhesive removal were performed with the Kruskal-Wallis and the Dunn’s multiple comparison test. The examiner calibration was performed by kappa coefficient analysis (k = 0.88). All statistical tests were calculated using STATA 12.0 for Windows (StataCorp LP, College Station, TX, USA). Significance for roughness tests was adopted with probability values p < 0.001 and for time removal p < 0.05.
10
Weight Gain Effects on Neurocognitive Outcomes
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The data are presented as median (interquartile ranges) or count (percentage). The Wilcoxon rank-sum test was conducted to compare the continuous variables and Fisher’s exact test was used for the categorical variables. Multivariate linear regression analysis was conducted to investigate the association between the changes in the z-score at each epoch with the cardiometabolic factors at school-age. Regression coefficients beta (β) with p values were calculated after adjusting for GA, birth weight z-score, weight z-score change from birth to discharge, and sex. Multivariate linear regression analysis was also used to determine the effect of weight gain at different epochs on each index scale of K-WISC-IV after adjusting for factors that could influence neurocognitive outcomes, such as GA, birth weight z-score, weight z-score change from birth to discharge, sex, and IVH27 (link)–30 (link). A p value of < 0.05 was considered statistically significant. STATA 12.0 for Windows (Stata Corp., College Station, TX, USA) was used to analyze all data.
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