Comparisons between groups were made using the 2-tailed, paired Student's t test or the Mann Whitney U test. Comparisons of serially changing values over time or dose between groups were done using general linear model (univariate or multivariate). Mouse survival curves were constructed using Kaplan–Meier method with a 60-day cutoff. The Log-rank or Breslow's test was used to compare the homogeneity of survival rates between categories. Two-tailed p values <.05 were considered significant. STATA/SE version 10.1 (StataCorp LP, College Station, TX) were used.
Stata se version 10
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STATA/SE version 10.1 is a statistical software package developed by StataCorp. It provides a comprehensive set of tools for data analysis, modeling, and visualization. The software supports a wide range of data types and offers a variety of statistical methods, including regression analysis, time series analysis, and multilevel modeling.
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32 protocols using stata se version 10
1
Comparative Statistical Analysis Protocol
2
Adipose Measures and Metabolic Biomarkers
3
ICGA-Guided Wound Excision Optimization
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Data were analyzed on an intention-to-treat basis using STATA/SE version 10.1. Data were reported as mean and SD for continuous variables and as number (%) for discrete variables. The difference between ICGA and clinical assessment marking was reported as the percent of difference, based on the following equation:
A statistician, who analyzed and reported data, was blinded to the study process. Using one-sample T-test, at least 20% of the absolute percent of difference was considered to be significant. Post-hoc subgroup analysis was conducted in 2 groups: decreased excision and increased excision. The aim of the analysis was to determine how much ICGA could reduce unnecessary excision of the wounds in the decreased excision group and how much ICGA could prevent inadequate excision in the increased excision group. All test statistics were one-sided, and P < 0.05 was considered statistically significant.
A statistician, who analyzed and reported data, was blinded to the study process. Using one-sample T-test, at least 20% of the absolute percent of difference was considered to be significant. Post-hoc subgroup analysis was conducted in 2 groups: decreased excision and increased excision. The aim of the analysis was to determine how much ICGA could reduce unnecessary excision of the wounds in the decreased excision group and how much ICGA could prevent inadequate excision in the increased excision group. All test statistics were one-sided, and P < 0.05 was considered statistically significant.
4
Wound Closure Outcomes Analysis
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Data were analyzed using STATA/SE, version 10.1. Data were reported as mean and SD for continuous variables and as number (%) for discrete variables. Binomial probability test was used to compare the complete wound closure to the expected rate of 80%. The McNemar test was used to determine the change in proportion for the dependent variables (short-term and long-term complete wound closure). Post-hoc subgroup analysis was conducted in 2 groups: superficial and deep groups. The aim of the analysis was to determine the wound outcome in the different types of wounds. All test statistics were 2-sided. P < 0.05 was considered statistically significant.
5
Multistage Sampling for Child Injury Epidemiology
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A multi-stage sampling process was employed. We assumed a 5% incidence of all cause child injury based on prior estimates in the region (Moshiro et al., 2005a (link)). The sample size with 80% power and 0.05 precision was calculated at 1,968 households with at least one child under age 19. The operational definition for a household was “the place where a caregiver and the child/children under their care regularly meet and share meals”. Sample size calculations were carried out using StataSE version 10.1. Each ward was allocated into one of six zones using ArcGIS version 9.2. Zones were determined based on ward population densities and distance from the central business district. A computer-assisted random sampling of two or three wards (depending on population density) by zone was then conducted, resulting in 17 selected wards. Within selected wards, a proportional sample of households was selected for visitation (1.1%) which yielded 2,131 households. Two wards (Vijibweni and Sandali) were removed to reduce over-sampling in areas that were demographically similar. An overview of the sampling procedure and sampled wards is provided in Fig. 1 .
6
Candida Infection Outcomes in Critical Care
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Analyses were performed using Stata/SE Version 10.1 (StataCorp LP, College Station TX). For the purpose of summarising case mix, outcomes and antifungal use, the cohort was divided into groups of: admissions with IFD positive for Candida albicans (Candida albicans IFD); admissions with IFD positive for other Candida species (non-albicans Candida spp IFD); and admissions with no IFD either prior to or during the critical care unit stay (no IFD). Admissions with IFD positive for Candida of unknown species or non-Candida species were excluded due to small numbers. Admissions with IFD positive for both Candida albicans and non-albicans Candida species were included in the Candida albicans subgroup.
7
Voice Outcome Measures Analysis
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Continuous variables are presented as the mean 6 SD and were compared among groups using 1-way ANOVA. Categoric variables are presented as counts with proportions and were compared among groups using the x 2 test. Changes in voice outcome measures and differences among groups over time were calculated using repeatedmeasures ANOVA. Post hoc testing with the Bonferroni method was performed when appropriate. To compare the F0, F max , and F min , only female patients were included in this analysis because of baseline sex-related differences. In this study, P values of less than 0.05 were considered statistically significant. Analyses were performed using STATA/SE, version 10.1 (StataCorp LP).
8
Determining Renal Dysfunction Prevalence
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The prevalence ratio (PR) of decreased GFR was estimated with generalizing linear models created assuming a Poisson distribution with logarithmic link function and robust variance. The covariates that were significantly associated with renal dysfunction in the univariate analysis (p<0.10) were entered in the multivariate models. Given the low prevalence of the study outcome, the variables were retained if they met the condition p≤0.10 in a backward elimination strategy and if they were considered potential confounders (e.g., when removed, a change equal to or greater than 20% in the prevalence ratio of any other variable of the model was observed). All of the analyses were performed using STATA/SE version 10.1.
9
Predicting Dropout in Clinical Trials
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Details of the statistical analysis are described in Appendix 1 , available online at www.thepcrj.org . Briefly, we analysed whether dropout was selective and missing predictor data were imputed using multiple imputation.17 (link) Twelve candidate predictors of all available data were preselected based on clinical expert opinion after removing five due to collinearity (Table 1 ). Bootstrapped (1000x) backward selection was used to select the final important predictors whose regression coefficients were shrunk to facilitate better performance when the model is applied to other data or future patients.18 (link) The predictive performance of the prediction index was assessed by its calibration and discrimination abilities.
Furthermore, the additional value of specific IgE was calculated by the additional area under the ROC curve (AUCdiff) and the net reclassification improvement (NRI).19 (link) All statistical analyses were carried out in Stata/SE Version 10.1 (Stata Corp, College Station, TX, USA).
Furthermore, the additional value of specific IgE was calculated by the additional area under the ROC curve (AUCdiff) and the net reclassification improvement (NRI).19 (link) All statistical analyses were carried out in Stata/SE Version 10.1 (Stata Corp, College Station, TX, USA).
10
Statistical Analysis of Experimental Data
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Initially, descriptive statistics were calculated, including means and standard deviations for continuous variables and frequencies and proportions for categorical variables. For ordinal variables or continuous variables that were not approximately normally distributed, we calculated medians and interquartile ranges. For continuous variables, independent t tests were used to evaluate between-group differences for all outcomes of interest. If the assumptions for the independent t test were not met, the nonparametric equivalent, the Kruskal-Wallis test, was used for intergroup comparisons. We used 2 × 2 contingency tables along with the chi-square statistic, or where appropriate, Fisher exact tests, to examine associations between categorical variables. All analyses were performed using Stata SE version 10.1, and a type I error rate established at P < .05.
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