Contingency tables were analyzed for differences in proportions among categorical variables between recipients with and without rejection using Fisher’s exact test. Differences in mean values of continuous variables were analyzed via Wilcoxon’s rank-sum test. Logistic regression models were used to evaluate odds ratio (OR). Statistical significance was defined as a p value < 0.05, allowing for multiple comparisons of main variables via Bonferroni’s procedure. All p values were 2-sided, and all estimates were done via the STATA statistical software (StataCorp. 2003. STATA statistical software. College Station, TX: Stata Corporation). Actuarial graft survival and freedom from TCAD was estimated using Kaplan–Meier analysis and statistical differences calculated with the log-rank statistic.
Stata statistical software
Manufactured by StataCorp
Sourced in United States, Austria, Denmark
Stata is a comprehensive statistical software package that provides a wide range of tools and functionalities for data analysis, modeling, and visualization. It is designed to handle various types of data, including cross-sectional, time-series, and panel data. Stata offers a powerful programming language, robust statistical methods, and a user-friendly interface, making it a widely-used tool in the fields of economics, social sciences, and healthcare research.
Automatically generated - may contain errors
Lab products found in correlation
Stata, by StataCorp (89 mentions)
Stata 13, by StataCorp (31 mentions)
Stata 15, by StataCorp (18 mentions)
Stata software, by StataCorp (13 mentions)
Stata version 15, by StataCorp (13 mentions)
Stata 14, by StataCorp (11 mentions)
Spss statistics for window, by IBM (10 mentions)
Stata 16, by StataCorp (10 mentions)
Stata 12, by StataCorp (10 mentions)
Stata 17, by StataCorp (8 mentions)
Sas software, by SAS Institute (8 mentions)
Spss software, by IBM (7 mentions)
Sas version 9, by SAS Institute (7 mentions)
Stata v 12, by StataCorp (5 mentions)
Stata 11, by StataCorp (5 mentions)
Stata statistical software release 15, by StataCorp (4 mentions)
Stata se, by StataCorp (4 mentions)
Stata version 14, by StataCorp (4 mentions)
Stata version 16, by StataCorp (4 mentions)
R software, by R Project for Statistical Computing (4 mentions)
1 123 protocols using stata statistical software
1
Evaluating Transplant Rejection Factors
2
Sociodemographic and Psychosomatic Factors Analysis
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For each question in the survey, we have identified and attributed a name of the corresponding variable (both qualitative and quantitative). The total number of collected and exploitable questionnaires with preliminary established masking was recreated within EpiData 3.1 statistical software programme (EpiData Association, Odense, Denmark, 2008)[24 ] and was entered for each submitted question for processing. Different sociodemographic, biological, reproductive, and psychosomatic variables were presented, compared, and analyzed. We exported the collected quantitative and qualitative variables to be further analyzed in a descriptive fashion and processed through STATA Statistical Software (StataCorp 11.0, 2009, STATA Statistical Software: Release 11. College Station, TX: StataCorp LP).[25 ] Kolmogorov–Smirnov test was used to analyze the distribution of the data, and one-way analysis of variance test was used to analyze the homogeneity of the data. Fisher's exact and Chi-square tests were used to compare the differences between groups. CI of 95% was calculated for percentage outcomes, and the results were considered statistically significant at P < 0.05 and nonsignificant at P > 0.05. The missing data (unanswered items on the questionnaire) were treated as N/A variables and included in the outcome analyses.
3
Seasonal Pollen Allergy Symptoms and Medication
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We used survey results from App users during the 2016 pollen season. We used a one-week control period before and after the TA period. Chi-square tests were used to explore the associations between sex, age, symptoms, and medication variables. Age was dichotomized as 18 to 40 years and 41 to 61 years. All the other variables i.e., asthma, hay fever, taking medication were categorical as yes or no. Symptom scores were ordinal with increasing in intensity from 1 to 5. Logistic regression was used to adjust the analysis of those variables to each other and between time periods. Two-sided p values less than 0.05 were considered as statistically significant. All analyses were performed using Stata statistical software (StataCorp. Stata statistical software: Release 16.1. College Station, TX).
4
Estimating Cancer Survival Using Relative Survival
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We estimated age‐standardized 5‐year relative survival as the ratio of the observed survival to the expected survival without taking cause of death into account. Expected survival was calculated based on the Ederer II method15 applied to all‐cause mortality tables for the canton of Zurich supplied by the Federal Statistical Office. Death probabilities based on age‐, sex‐, and calendar year‐specific death rates were interpolated and smoothed using the Elandt‐Johnson formula.16 Relative survival was developed as an estimator of net survival, which is commonly used when estimating patient survival using data from population‐based cancer registries.12 Relative survival was estimated using the strs command in Stata Statistical Software (StataCorp LP, version 15). Cohort analyses were used to derive relative survival estimates for all time periods (2010–2015 for the latest period in order to provide at least 5 years of follow‐up). All analyses were performed using Stata Statistical Software (SE version 15).
5
Statistical Analysis of Cell Characteristics
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Quantitative processing of the material was performed using the Descriptive Statistics programs available in the Microsoft Excel 2010, Statistica 12 packages, and STATA statistical software (StataCorp. 2012. STATA statistical software: Release 12. College Station, TX: StataCorp LP, USA). Prior to the experiments, we performed a statistical analysis based on the variations in the measured parameters in our previous research [15 (link)] and determined that we needed a group of at least 4 animals to achieve the statistical confidence at 95%. To make sure that we reach a group size of 4 and, at the same time, reduce the use of animals to a minimum, we aimed at total of 5 animals per experimental group. The distribution density and dimensional characteristics of the cells were estimated using the methods of variation statistics. To quantify the results, mean values and standard deviation (M ± SD) were found and analyzed with the SPSS software (version 16.0; SPSS Inc., Chicago, IL, USA). All variables measured in groups were compared using Student–Newman–Cales test or a one-way analysis of variance (ANOVA, Chicago, IL, USA) with Bonferroni correction. Values at p ≤ 0.05 were considered statistically significant.
6
Retrospective Analysis of Shoulder Outcomes
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Given that this was a retrospective analysis, the availability of data determined the sample size. A power analysis was performed to assess the capability of the sample size to detect a clinically meaningful change in the ASES score from pre- to postoperative. Assuming a standard deviation of 20 points, a sample size of 13 patients would provide a power of 80% to detect a 17-point difference between pre- and postoperative ASES scores at a level of .05.
Descriptive statistics—including means ± standard deviations for continuous variables and frequency and proportion for categorical variables—were calculated to characterize the study groups. Preoperative, postoperative, and delta (change in scores from pre- to postoperative) ASES, SST, Rowe, Constant-Murley, SANE, and VAS pain scores were compared using an independent t test after confirming that the data were normally distributed. Results of inferential analysis are presented as 95% CIs. P < .05 was considered statistically significant. All analyses were performed with Stata statistical software (StataCorp 2017, Stata statistical software: Release 15; StataCorp).
Descriptive statistics—including means ± standard deviations for continuous variables and frequency and proportion for categorical variables—were calculated to characterize the study groups. Preoperative, postoperative, and delta (change in scores from pre- to postoperative) ASES, SST, Rowe, Constant-Murley, SANE, and VAS pain scores were compared using an independent t test after confirming that the data were normally distributed. Results of inferential analysis are presented as 95% CIs. P < .05 was considered statistically significant. All analyses were performed with Stata statistical software (StataCorp 2017, Stata statistical software: Release 15; StataCorp).
7
Demographic and Clinical Assessments
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Descriptive statistics were computed for demographic characteristics. Clinical assessment data were coded according to their respective guidelines and relevant cut-off points. Percentages, means (M) and standard deviations (SDs) or medians (Mdn) with interquartile ranges (IQRs) were calculated for each variable. Statistical analyses were performed using Stata Statistical Software (StataCorp 2015, Stata Statistical Software: Release 14.0, College Station, TX, StataCorp LLC).
8
Analysis of Tumor Diameter Associations
9
Exploring Inflammatory Biomarkers in Migraine
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This study presented continuous variables as means ± standard deviations and medians. Due to the small sample sizes, comparisons between patients and healthy volunteers were performed with Wilcoxon signed rank tests. An exploratory sensitivity analysis was undertaken using a hierarchical linear model using random effects of the matched pair. For distributions that were non-normal, estimates were checked using transformations. If interpretation of the results did not change, the original model results were reported. Results are provided for unadjusted differences in means between groups as well as estimates adjusted for chronic migraine and meeting criteria for medication overuse headache. Our statistical analysis was performed using Stata (StataCorp. 2019. Stata Statistical Software: Release 16. College Station, TX: StataCorp LLC.). Fluorescence-Activated Cell Sorting (FACS) data were processed using FlowJo software (Ashland, OR: Becton, Dickenson and Company; 2019). Cytokine multiplex data were processed using Stata (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC.).
10
Neonatal Mortality Trends Across Prematurity
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Statistical analysis was performed using Stata Statistical Software (StataCorp. 2021. Stata Statistical Software: Release 17). Descriptive data were presented using proportion, mean and standard deviation or median and interquartile range as appropriate. Differences in prematurity groups or periods were compared by chi-square test or Kruskal-Wallis rank test. Percentage change in mortality within prematurity groups was calculated using the formula:
Percent change in mortality = [(mortality in 2015–2017 – mortality in 2008–2011)/mortality in 2008–2011]*100.
Incidence rates for early neonatal deaths (0–7 days), later neonatal deaths (8 (link)–20 , 27 (link)–34 (link)) and infant deaths were calculated for each prematurity groups and Kaplan-Meyer curves created. SCBU stay duration and secondary outcomes were evaluated from medical charts of neonates who survived the early neonatal period, and for whom data were available from the first 24h of life.
Percent change in mortality = [(mortality in 2015–2017 – mortality in 2008–2011)/mortality in 2008–2011]*100.
Incidence rates for early neonatal deaths (0–7 days), later neonatal deaths (8 (link)–20 , 27 (link)–34 (link)) and infant deaths were calculated for each prematurity groups and Kaplan-Meyer curves created. SCBU stay duration and secondary outcomes were evaluated from medical charts of neonates who survived the early neonatal period, and for whom data were available from the first 24h of life.
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