For statistical analysis, the STATA program (Stata Corp., College Station, Texas, USA) was employed. Counts or percentages are reported for categorical data, whereas means and standard deviation (SD) are shown for continuously distributed variables with a normal distribution. Interquartile ranges (IQR) are presented for continuous variables with a non-normal distribution. For continuous data, the Wilcoxon rank-sum test was used for non-normally distributed variables and the independent t-test for normally distributed variables. To evaluate the relationship between PAC following an ACTH stimulation test and PA diagnosis, multivariable logistic regression analysis was employed. The data is displayed as unadjusted and adjusted odds ratios (ORs) and 95% confidence intervals (CI). The model was adjusted for age, sex and body mass index (BMI). Area under the receiver operating characteristics (AuROC) and the selection of optimal cut-off values were performed only for values which demonstrated statistically significant results in multivariable analysis. The optimal cut-off value was selected by Liu index [17 (link)]. Statistical significance was set at p < 0.05. The sample size calculation was not performed due to this was a pilot study. Missing data and inclusive results were excluded from the study.
Stata program
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STATA is a general-purpose statistical software package developed by StataCorp. It is designed for data management, statistical analysis, and graphics. STATA provides a wide range of statistical techniques, including regression analysis, time series analysis, and survey data analysis. The software offers a command-line interface as well as a graphical user interface for easy data manipulation and visualization.
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29 protocols using stata program
1
Adrenal Cortex Hyperfunction Diagnosis
2
Meta-analysis of Chitosan Efficacy
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We calculated weighted mean difference (WMD) and 95% CIs between the intervention and control groups for continuous outcomes. Heterogeneity across the studies was tested using the I2 statistic, and an I2 value of >50% indicated significant heterogeneity. Primary analyses were conducted with a fixed-effects model, and secondary confirmatory analyses were conducted with a random-effects model if there was significant heterogeneity.18 (link) To examine the effects of factors on the primary outcomes, several previously defined subgroup analyses were considered according to the intervention duration, study design, and chitosan dose. In order to evaluate the influence of each study on the overall effect size, sensitivity analysis was conducted by removing one study at a time or changing the effects models independently. Presence of publication bias in the meta-analysis was assessed by visually inspecting a funnel plot and was also evaluated by using the Egger’s test.19 (link) All tests were two-tailed and a P-value of <0.05 was considered statistically significant for all analyses. All statistical analyses were performed using the STATA program (Version 12.0; StataCorp LP, College Station, TX, USA).
3
Evaluating Repeated Measures Intervention Outcomes
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Data were analyzed using Stata program (StataCorp. 2017. Stata Statistical Software: Release 15. College Station, TX: StataCorp LLC). For categorical variables, frequencies and percentages of were recorded; for continuous variables, means and standard deviations. Demographic data were analyzed using Fisher’s exact test for categorical variables, the independent t-test for normally distributed continuous variables and the Mann-Whitney U test for non-normally distributed continuous variables. The paired t-test was used to evaluate the outcomes at each follow-up visit (weeks 1, 4, 12 and 24 after treatment) compared to the baseline. Differences between the two treatment groups at each follow-up visit were analyzed using mixed-model analysis of repeated measures including measurement of the effect of treatment, time, severity and interaction between treatment and time. Statistical significance was accepted at p < 0.05.
Estimated sample size for two-sample comparison of means with repeated measures. The preliminary data of BQ scores were used to calculate the sample size in each group [alpha = 0.05 (two-sided), power = 0.9. mean in population 1 = 22.33, mean in population 2 = 29, SD in population 1 = 2.51, SD in population 2 = 3]. Estimated required sample size in each group was 4.
Estimated sample size for two-sample comparison of means with repeated measures. The preliminary data of BQ scores were used to calculate the sample size in each group [alpha = 0.05 (two-sided), power = 0.9. mean in population 1 = 22.33, mean in population 2 = 29, SD in population 1 = 2.51, SD in population 2 = 3]. Estimated required sample size in each group was 4.
4
Biochemical Factors and Adrenal Insufficiency
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Data were analyzed using the STATA program (Stata Corp., College Station, TX, USA). Categorical variables are expressed as a number or percentage, and continuous variables are expressed as means and SDs. Statistical analysis of categorical variables was done using the Fisher exact test, while continuous variables were compared using the t-test or Mann-Whitney U test, as appropriate. Univariable analysis was performed by dividing the cohort into 2 groups, which were patients with and without biochemical AI. Multivariable logistic regression analysis was conducted with clustering by ACTH dose (1 or 250 μg) to determine the influence of different biochemical factors on the occurrence of biochemical AI, and odds ratio (ORs) are presented. Demographic and biochemical variables were used to adjust the model. A final diagnostic prediction model was performed by stepwise logistic regression analysis. Stepwise regression analysis was performed manually to identify the smallest number of variables as possible with the highest area under the receiver operating characteristic curve (AUC-ROC). The AUC-ROC of the model was calculated to assess the diagnostic performance. A two-tailed p-value < 0.05 was considered statistically significant.
5
Wound Healing Probability Analysis
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All statistical analyses were calculated using the Stata program (StataCorp, College Station, Texas). Kaplan-Meier survival analysis and a log-rank test were used to estimate healing probabilities for stratified baseline wound areas of 15 cm2 or less, between 15 and 25 cm2, and 25 cm2 or larger. A Bonferroni correction was applied to the P value calculated through the log-rank test. A Cox proportional hazard model was used to assess the ability of baseline wound area size to predict healing outcomes. The proportional hazard assumption was met. A 95% confidence interval (CI) was calculated and included where appropriate. All statistical significance was analyzed with a two-sided α of .05. Continuous data are expressed as mean ± SD unless indicated otherwise. Safety and demographic analyses were presented using the intent-to-treat population. Efficacy analyses followed the published literature12 (link) by presenting the per-protocol population first to provide a fair comparison.
6
Autopsy Diagnosis Discrepancy Analysis
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False-negative cases contained class I and II discrepancies in which the autopsy diagnosis was in the assessed diagnostic category but the clinical diagnosis was in another category. False-positive diagnoses were cases with major discrepancies (class I and II), in which the clinical diagnosis was in the assessed diagnostic category but not the autopsy diagnosis. The concordance between raters was assessed by the Kappa statistic, which adjusts for chance agreement, and interpreted as suggested by Landis and Koch [20 (link)]. Proportions were compared by chi-square test, and logistic regression with penalised likelihood was used to evaluate factors associated with major clinical errors [21 (link),22 (link)].The sensitivity, specificity, positive (PPV) and negative predictive values (NPV) for each diagnosis were calculated. Data were analysed with the STATA program (Version 15, StataCorp 2017, College Station, TX, USA).
7
Diagnostic Accuracy Evaluation in STATA
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Statistical analysis was conducted using the STATA program (version 13, Stata Corp., TX, USA). Sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and accuracy were determined with 95 % confidence intervals (CIs).
8
Dietary Guide Adherence Determinants
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Descriptive data were presented as absolute and relative frequency or as mean and standard deviation. The Kolmogorov–Smirnov test was used as a normality test. Continuous variables were compared between genders using Student’s t test and categorical variables using Pearson’s chi-squared test. For the association between adherence to the dietary guide (dependent variable) and associated factors (independent variables), a multiple linear regression model was used. The selection of covariates was based on the literature, as well as on the presence of confounding factors and on the collinearity between variables. Effect measurements were presented as beta values and p values. Statistical analyses were performed using the Stata program (version 12.0, 2011, StataCorp LP, College Station, TX, USA). p < 0.05 was considered significant.
9
Statistical Analysis of PELD Scores
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Data were analyzed by using STATA program (StataCorp. Version 14, College Station, TX). Comparisons between the low PELD score and the high PELD score groups were examined by using chi-squared test and Fisher’s exact test for categorical variables with normal and non-normal distribution, respectively. Similarly, Student’s t-test and Mann–Whitney U test were applied for continuous variables. P < 0.05 was considered statistically significant.
10
Correlation of Fibrinogen and ROTEM in Patients
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From a review of the literature, the correlation coefficient between fibrinogen level and the FIBTEM MCF was obtained from a study by Meyer et al. [5 (link)]. The correlation coefficient was 0.64, and after calculation with a sample size formula, the final sample size was 40 patients, but we included a total of 87 patients to improve the power of the study. The data record tables were fulfilled by reviewing the inpatient department medical record computer program. The statistical analysis was performed with the STATA program (version 12.1). Nonparametric data are reported as median and interquartile ranges (IQR). Linear correlation was analysed using the Pearson method. A receiver operating characteristic curve analysis was performed to evaluate the performance of the ROTEM instrument with respect to discriminating patients with admission fibrinogen levels. Linear regression was used to assess the correlation between ROTEM instrument factors and fibrinogen level. A P value less than 0.05 was chosen to represent statistical significance throughout. The study was approved by the Prince of Songkla University, Human Research Ethics Committee, Faculty of Medicine.
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