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Stata se 10

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Stata/SE 10.0 is a statistical software package developed by StataCorp. It provides a range of data management, analysis, and visualization tools for researchers and professionals. The software supports a wide variety of statistical methods, including regression analysis, time series analysis, and multilevel modeling. Stata/SE 10.0 is designed to handle large datasets and offers advanced features for data manipulation and programming.

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40 protocols using stata se 10

1

Anaemia Risk Factors Analysis

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STATA/SE 10.1 was utilised to analyse the data. Multinomial logistic regression was used to establish the RR of independent variables known to cause anaemia including: age, gender, renal impairment, ACEIs use, and aspirin use. This statistical method was also used to establish the RR of two of the ACEIs most used in our cohort of patients, namely perindopril and ramipril. Student’s t-test (two tailed) was used to compare the Hb levels between patients on ACEIs and those who were not on ACEIs. A 2x2 contingency table was made to compare the RR of different aspirin doses. Statistical significance was declared at a P value of less than 0.05.
Power analysis of this study indicates that patient numbers were sufficient for its objective. The multiple logistic regression had five independent variables, and thus a patient number of 10-20 times this number of variables should be used.
11 (link)
This study included 96 patients, which is 19 times the number of variables.
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2

Assessing Arterial Oxygen Levels and Lung Injury

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The partial pressure of arterial oxygen (PaO2) at the end of the experiment was defined as the primary outcome. Inflammatory markers and markers of lung injury were assessed as secondary outcomes. Based on previous pilot results and with a group size of eight animals per group the Wilcoxon rank-sum test ensures 80% power using a two-sided significance level of 0.05 to detect an estimated effect size of 1.85 that the observed parameter differs between groups. With an anticipated dropout rate of one per group, nine animals were initially assigned to each group. Differences between study groups were tested with one-way analysis of variance or Kruskal Wallis as appropriate. Cuzick’s test was used to test for trends across different FiO2 groups. Statistical analyses were performed using R version 3.2.1 (R Foundation for Statistical Computing, Vienna, Austria) and STATA/SE 10.1 (StataCorp LP, College Station, TX, USA).
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3

Evaluating Helicobacter Pylori Eradication Rates

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For the sample size calculation, we hypothesized that there would be approximately 18% difference in the eradication rates between the two regimens. Knowing that the eradication rate of STT was approximately 72%, our sample size estimation was 85 for each group, given a power of 80% and a confidence level of 95%, assuming a 20% loss to follow up (Jianjaroonwong, 2013).
Statistical differences in eradication rates among the different regimen were assessed by chi-square test. The demographic data and frequencies of adverse reactions were compared using chi-square test or Fisher’s exact test, when appropriate. The p-values < 0.05 were considered to be statistically significant. The statistical analyses were performed using the Stata/SE 10.1.
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4

Survival of Elderly Solid Tumor Patients

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We performed a cross sectional cohort study using the registry of Hospital Israelita Albert Einstein as our database. This is a private hospital located in the city of São Paulo (SP), Brazil. Patients aged 65 or older who were diagnosed with solid tumors, and registered between January 2007 and December 2011, were the subjects of this analysis.
The charts and electronic institutional databases were reviewed to obtain information about sex, age at diagnosis, type of cancer, stage at diagnosis, and overall survival. We were not able to establish cancer-specific and all-cause mortality for a large proportion of patients.
Overall survival was defined as duration of time from diagnosis until death and patients were censored if they were lost to follow-up (collected until March 21, 2013). Overall survival was estimated using Kaplan-Meier methods. The impact of each treatment on survival was calculated using a Cox regression model.
TNM Classification of Malignant Tumors, seventh edition, was used to describe and categorize all different cancer stages in this article.
This project was approved by the Institutional Ethics Committee, CAAE: 19159813.3.0000.0071. All analyses were performed on Stata SE 10.1 (StataCorp. College Station, Texas, United States) with a two-sided alpha-value of 0.05, unless otherwise stated.
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5

Maternal Factors and Postpartum Checkup

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Univariable log binomial regression was used with sampling, non-response, and non-coverage weights, accounting for stratum assignment and sample design. Risk ratios (RRs) were calculated with 95% confidence intervals (CI) to assess the associations between each maternal factor and a missed postpartum checkup. Analyses were performed using SAS 9.3 (SAS Institute, Inc., Cary, NC) and STATA/SE 10.1 (StataCorp, LP. 2007, College Station, TX).
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6

Statistical Analysis of ESI-dexa vs ESI-beta

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Statistical analysis was performed for demographic data using a t-test between the ESI-dexa and ESI-beta groups, as well as on an intention-to-treat basis for primary and secondary outcomes, involving participants who were available at the 2-week follow-up. Missing data were dealt with by using the last observation carried forward method, therefore, there was no censored data during the follow-up period. For a binary outcome, a two-sample test of proportions and a chi-square test were used. Continuous outcomes, as well as VAS and disability scores, were analyzed with the t-test and the linear mixed model using the delta method [27 ], in which VAS and disability scores were considered dependent variables and the follow-up period and the drugs, as explanatory ones. Statistical analyses were performed using statistics software (STATA/SE 10.1; Stata Corp LP, College Station, TX). A P value of less than 0.05 was considered to indicate a significant difference.
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7

Cytokine Profiling Using Flow Cytometry

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The results were expressed as mean ± standard error of mean (SEM). Fisher’s exact test and the χ2 test were used to detect significant differences in categorical variables. Testing for differences in mean proportions was performed when appropriate with z tests. The non-parametric Wilcoxon’s rank sum test was used to detect significant differences in the median values of continuous variables. A two-tailed P value of 0.05 or less was considered to be statistically significant. The data were analyzed using the STATA SE 10.1 program (STATA Corp, College Station, TX, USA). Data for each kit were analyzed as recommended by the manufacturer. The cytokine concentration in each analyte was obtained by interpolating the fluorescence intensity of at least a 7-point dilution standard curve supplied with the kit and calculated by SoftMax® Pro Data Acquisition & Analysis Software and BD FCAP™ Array Software.
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8

Comparative Statistical Analysis Protocol

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Results are presented as the mean ± SD. Data were analyzed using GraphPad Prism 6 software for Windows (GraphPad Software, Inc, La Jolla, CA, USA). Statistical analyses were performed using Stata/SE 10.1 software (Statacorp, College Station, Texas, USA). A two‐tailed Student's t test was used to compare data between two groups, and one‐way ANOVA was used to compare data between three groups with the Scheff multiple‐comparison test. For miRNA microarray assay, fold changes of 2 or more and a P‐value < 0.05 were considered statistically significant. For all other tests, P < 0.05 was considered statistically significant.
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9

Physician and Nurse Perspectives in ICU

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All data are expressed as percentages of the total number of respondents for the particular questions, unless otherwise specified. Data are presented as means (±SD) or medians (interquartile range (IQR)) depending on data distributions, unless stated otherwise. For assessing differences between physicians and nurses, ICU personnel were grouped according to their respective profession and data were analyzed using Fisher's exact tests. ICU physicians, fellows, and residents were classified as physicians; ICU nurses and ICU nurses in training were classified as nurses.
Statistical analyses were conducted using STATA/SE 10.1 (StataCorp LP, College Station, TX, USA).
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10

Biomarker Comparison across Pregnancy Stages

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To compare biomarker concentrations between recruitment, delivery and postpartum, a two-way crossed-effects model was estimated, with the subject effect being crossed with the site effects. For this objective, pairwise statistical significance was interpreted based on 95% confidence intervals, considering significant when the interval did not include 0. To compare the biomarker concentrations between paired periphery and placental plasma samples or paired periphery and cord plasma samples, a Wilcoxon matched-pairs signed rank test was performed. P-values were corrected for multiple comparisons with the Benjamini-Hochberg test. To compare biomarker concentrations among sites at delivery, a Kruskal-Wallis test was performed followed by Dunn's Overall, significance was defined at p<0.05. Analyses and graphs were performed using Stata/SE 10.1 (College Station, TX, USA) and GraphPad Prism (La Jolla, CA, USA).
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