Patients’ biodata and the presence or absence of multiple strain infection results were entered and validated in Microsoft Excel® 2013. The data was then exported to Stata (Stata/SE 14.2 for windows, Stata Corp, College Station, TX) for statistical analysis. Chi-square test was used to compute proportions and determine the relationship between independent factors and dependent variables (presence multiple strain infection) with statistical significance considered at a 95% level of confidence. Since the feature of interest (multiple strain infections) was found in a small number of patients, an exact bivariate logistic regression analysis was performed to obtain odds ratios for factors that could be associated with the occurrence of multiple strains of M. tuberculosis among PTB patients in our setting. Exact logistic regression was selected because it calculates the conditional maximum chance of an event occurring within the sample population described by the model's varying factors. We did not specify a statistical significance threshold as per the recent statistical guidelines47 (link),48 .
Stata se 14.2 for windows
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10 protocols using stata se 14.2 for windows
1
Identifying Multiple Strain Tuberculosis
2
Labor Force Dynamics in Population Survey
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Descriptive statistics were performed using frequencies and percentages.
Two-sample and paired t-tests were used for comparisons between
the groups of PSC and controls. All variables were stratified by gender.
Stata/SE 14.2 for Windows was used for the analyses. Tables and results from the
statistical analyses, confidence intervals and statistical significance, are
presented inSupplemental Tables II –IV .
We calculated the proportions of the total sample at the same time each year. All
data for variables labor-force participation, working hours,
and job changes were monitored every 12th month;
working hours and job changes were
dependent on labor-force participation. Significance level was
5%.
Two-sample and paired t-tests were used for comparisons between
the groups of PSC and controls. All variables were stratified by gender.
Stata/SE 14.2 for Windows was used for the analyses. Tables and results from the
statistical analyses, confidence intervals and statistical significance, are
presented in
We calculated the proportions of the total sample at the same time each year. All
data for variables labor-force participation, working hours,
and job changes were monitored every 12th month;
working hours and job changes were
dependent on labor-force participation. Significance level was
5%.
3
Mobility in Persons with Lower Limb Amputations
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The current observational study is part of a larger project examining mobility in persons with LLA. The Regional Committee for Medical and Health Research Ethics in Norway (2015/1245) approved the study which was performed according to the principles of the Declaration of Helsinki. All participants signed an informed consent prior to participation.
The main hypothesis was to investigate betweengroup comparisons. To identify the minimum sample size, an a priori power analysis was conducted based on walking speed (Wezenberg et al., 2013) (link). Power was set to 90%, alpha to .05 and power analysis (by Stata/SE 14.2 for Windows, College Station, TX, USA) estimated to a total sample size of 20. To increase the robustness of the analyses, a sample size of more than 30 persons in each group was considered appropriate.
The main hypothesis was to investigate betweengroup comparisons. To identify the minimum sample size, an a priori power analysis was conducted based on walking speed (Wezenberg et al., 2013) (link). Power was set to 90%, alpha to .05 and power analysis (by Stata/SE 14.2 for Windows, College Station, TX, USA) estimated to a total sample size of 20. To increase the robustness of the analyses, a sample size of more than 30 persons in each group was considered appropriate.
4
Accuracy Evaluation of SARS-CoV-2 Diagnostic Tests
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Data were assessed using STATA SE 14.2 for Windows (StataCorp LP, College Station, TX, USA) and MedCalc® Version 20.114 (MedCalc Software Ltd., Ostend, Belgium) software packages, with raw data being captured in Microsoft® Excel (Redmond, WA, USA). The positive percentage agreement (PPA), negative percentage agreement (NPA) [37 (link)], agreement (Cohen kappa [38 (link),39 ]) and precision compared to SOC (both tests) and Xpert® Xpress SARS-CoV-2 (Xpert® Xpress CoV-2 plus only) were calculated; 95 percent confidence intervals (95% CI) were included. Errors and invalid specimens were noted, and these results were excluded from the accuracy analysis.
5
Statistical Analysis of Microcarcinoma Characteristics
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Summary statistics were reported as numbers (percentages) or mean ± standard deviation. Categorical variables were analyzed with the X2 test or the Fisher exact test, as appropriate. Continuous variables were compared between groups using the Student t test or one-way analysis of variance and Bonferroni multiple-comparison test. Univariate and multivariate conditional logistic regression models were performed in order to analyze the relationship between variables and mPTC groups. Conditional logistic regression models were conducted on pertinent clinicopathologic variables that reached p < 0.2 at univariate analysis. Model building followed a backward-stepwise approach, and the test of term significance was the Wald chi-square test.
Stata SE 14.2 for Windows (StataCorpLp, College Station, TX, USA) and GraphPad Prism, (GraphPad Software, San Diego, CA, USA) software were used for statistical analyses. All p values refer to two-tailed tests of significance; p < 0.05 was considered statistically significant.
Stata SE 14.2 for Windows (StataCorpLp, College Station, TX, USA) and GraphPad Prism, (GraphPad Software, San Diego, CA, USA) software were used for statistical analyses. All p values refer to two-tailed tests of significance; p < 0.05 was considered statistically significant.
6
Statistical Analysis of Outcomes
7
Statistical Analysis of Confidence Intervals
8
Post-Donation Kidney Function and Graft Outcomes
9
Statistical Analysis of Research Data
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Descriptive statistics are reported using t tests, chi-square tests, and Pearson’s correlation coefficient. An α of 0.05 was considered significant. Confidence intervals are reported as per the method of Louis and Zeger [40 ]. Analyses were performed using Stata 14.2/SE for Windows (College Station, Texas).
10
Statistical Analysis of Experimental Data
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