For the patient characteristics, continuous variables were analyzed using the Student’s t test, and categorical variables were analyzed using the chi-square or Fisher’s exact test. A p value of < 0.05 was considered statistically significant. The potential risk factors were analyzed by univariate and multivariate analyses using a Binary logistic regression model with stepwise and best subset approach for variable selection (Zhang, 2016 (link)). Odds ratios (OR) and 95% confidence intervals (CI) were computed to assess the strength of the associations between the various factors and PHLF. A p value of < 0.05 was considered statistically significant. Analyses were performed using STATA program version 14 (StataCorp, College Station, TX, USA). The cut-off value of AST, ALT, and INR post-hepatectomy were determined by receiver operating characteristic (ROC) curve analysis. The values that provide the most accuracy for PHLF prediction were selected as cut-off values. Post-hoc power analysis for two-sample comparison of proportions to detect a statistical alpha of 5% significant level (two-sided) revealed a power of 0.83.
Stata program version 14
Manufactured by StataCorp
Sourced in United States
STATA program version 14 is a comprehensive statistical software package developed by StataCorp. It is designed to facilitate data analysis, management, and visualization. The program offers a wide range of statistical tools and techniques, including regression analysis, time series modeling, and data manipulation capabilities. STATA version 14 is suitable for a variety of research and professional applications across multiple disciplines.
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Lab products found in correlation
18 protocols using stata program version 14
1
Predicting Postoperative Liver Failure
2
Hernia Recurrence and Risk Factors Analysis
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All data were collected in an electronical database and processed by STATA program version 14 (StataCorp LP 4905 Lakeway Drive College Station, Texas 77845 USA). Numerical data were expressed as mean and standard deviation or median and range, as appropriate. Categorical data were expressed as frequency and percentage. Variables were assessed according to hernia type, IH or PH. Recurrent cases were compared with all non-recurrent cases. Chi-square test (Fisher’s exact test) was used to examine the relationship between categorical variables, statistical differences for continuous variable for groups were examined using Student’s t test. The Tukey–Kramer pairwise comparisons was applied for variable fixation studentized range with significance according to the calculated critical value. Survival analysis was performed using the Kaplan–Meier method and comparisons between recurrence was assessed with a log-rank test. Multivariate analysis was performed for incisional hernia only, as there were too few recurrent cases in primary hernia subgroup to justify analysis, and it was made according to the Cox-regression hazard model (including significant factors of from univariate analysis) expressed as Hazard ratio (HR) with it 95% confidence interval (CI). A p-value < 0.05 was considered significant.
3
Statistical Analysis of Patient Outcomes
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Descriptive statistics, i.e. number with percentage or mean with its standard deviation (SD) along with 95% confidence intervals (CI) as appropriate, or median with interquartile range (IQR) were used to present the patient characteristics and study outcomes. The continuous variables between Group A and Group B were compared by Student's t-test or Mann-Whitney test, as appropriate. Chi-square test or Fisher's exact test were used to test the difference in proportion of categorical variables between the two groups. Uni- and multiple regression analysis were used to explore the correlation between ICIQ-UI score and other variables. Analysis statistics were performed using STATA program version 14 (StataCorp, College Station, TX, USA). A p-value of <0.05 was considered indicative of statistically significant differences.
4
Prognostic Role of Platelet-Lymphocyte Ratio
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Categorical and numerical variables were analyzed using Pearson’s χ2 test and the Mann–Whitney test, respectively. Univariate and multivariate analyses were conducted using the logistic regression model. Odds ratios (OR) and 95% confidence intervals (CI) were computed to assess the strength of the associations between the various factors and the outcome. A p value of <0.05 was considered statistically significant. Analyses were performed using STATA program version 14 (StataCorp, College Station, TX, USA). The cut-off value for high and low PLR, 102, was determined by receiver operating characteristic (ROC) curve analysis.
5
Survival Analysis of Surgical Oncology
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Quantitative variables are expressed as the mean ± standard deviation (SD). Categorical variables are reported as frequencies and percentages. For the univariate analysis, the Chi square test (or exact Fisher test in small samples) was used to compare two qualitative samples; the Student t-test was used to compare two quantitative samples; and the ANOVA test was used to compare more than two quantitative samples.
The follow-up time we considered was from the date of surgery until the day of death, or the last day of follow-up in patients who did not die. This was because the tumour registry did not contain any clear definition of the date of diagnosis. Survival analysis was performed using the Kaplan–Meier method and the log-rank test was implemented to estimate the differences between groups in terms of OS and DFS. Probability values of P < 0.05 were accepted as the statistical significance cut-off level. Statistical analysis was carried out with the IBM SPSS Statistics® program version 22 (IBM®, Armonk, New York, USA). The CUSUM curves were calculated using the STATA® program version 14 (StataCorp LP®, College Station, Texas, USA).
The follow-up time we considered was from the date of surgery until the day of death, or the last day of follow-up in patients who did not die. This was because the tumour registry did not contain any clear definition of the date of diagnosis. Survival analysis was performed using the Kaplan–Meier method and the log-rank test was implemented to estimate the differences between groups in terms of OS and DFS. Probability values of P < 0.05 were accepted as the statistical significance cut-off level. Statistical analysis was carried out with the IBM SPSS Statistics® program version 22 (IBM®, Armonk, New York, USA). The CUSUM curves were calculated using the STATA® program version 14 (StataCorp LP®, College Station, Texas, USA).
6
Statistical Analysis of Experimental Data
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Statistical analysis was done using STATA program version 14 (StataCorp LP 4905 Lakeway Drive College Station, TX, USA). Numerical data were expressed as mean and standard deviation. Student's t‐test was used to assess differences between two conditions. p‐Values ≤ 0.05 were considered statistically significant
7
Impact of Scene Time on Mortality
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The sample size was calculated by a two-sample comparison of proportions method, in which the statistical power of 90% and statistical significance level of 5% were determined, resulting in an estimated sample size of 86. Due to an error margin of 20%, the total sample size was determined to be 104.
Continuous data were summarized as mean and standard deviation, median, and interquartile range as appropriate. Student’s t-test or Wilcoxon Rank Sum test was employed to identify significant differences between the patients who died within 24 h and those who did not, based on the data distribution. Categorical variables were analyzed with the Chi-square test or exact probability test and reported as percentages. Multivariable risk regression analysis was used to evaluate the independent effect of scene time on 24-h mortality. A P < 0.05 was statistically significant. We used STATA program version 14 (StataCorp, College Station, TX, USA) for data analysis.
Continuous data were summarized as mean and standard deviation, median, and interquartile range as appropriate. Student’s t-test or Wilcoxon Rank Sum test was employed to identify significant differences between the patients who died within 24 h and those who did not, based on the data distribution. Categorical variables were analyzed with the Chi-square test or exact probability test and reported as percentages. Multivariable risk regression analysis was used to evaluate the independent effect of scene time on 24-h mortality. A P < 0.05 was statistically significant. We used STATA program version 14 (StataCorp, College Station, TX, USA) for data analysis.
8
Comorbidities and CAR Development
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Descriptive data are summarized as mean and standard deviation (SD), median and interquartile range (IQR), or percentage, as appropriate. All variables were compared between patients who experienced rashes and those who did not. The unpaired t-test was used for continuous variables and the chi-square test or exact probability test was used for categorical variables. Multivariable logistic regression analysis was used to demonstrate the relationship between comorbidities and CAR development. A p value less than 0.05 was statistically significant. STATA program version 14 (StataCorp LLC, College Station, TX, USA) was used for the data analysis.
9
Statistical Analysis of Risk Factors
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Categorical variables were analysed using Pearson’s χ2 test (or Fisher’s exact test if appropriate), while numerical variables were analysed using independent Student’s t-test (or the Mann-Whitney U test if appropriate). Risk factors with a p value of < 0.1 in univariate analysis were entered into multivariable analysis with applied logistic regression. Odds ratios (ORs) and 95% confidence intervals (CIs) were calculated to assess the strength of the associations between the variables and the outcomes. The performance of the final model was described using receiver operating characteristic curves. A p value of < 0.05 was considered statistically significant. Analyses were performed using the STATA program, version 14 (StataCorp, College Station, TX, USA).
10
Surgical Management of cSCCs in EB
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A monocentric, retrospective analysis was performed on EB patients treated at the Dermatological Clinic of the Modena University Hospital. We included patients affected by EB with histologically diagnosed cSCCs and treated with surgical excision at our clinic, from inception to the present.
We collected the following clinical data for each subject: age and sex, date of cSCC diagnoses, number of cSCCs, relapses, site of the neoplasm, number of surgical interventions needed for radical excision, use of dermal substitutes, and infections in the postoperative period.
Statistical analysis was performed with the STATA program, version 14 (StataCorp LP 4905 Lakeway Drive College Station, Texas 77,845 USA). Numerical data were expressed as mean and standard deviation or median and range as appropriate. Qualitative data were expressed as frequency and percentage. The chi‐square test (Fisher’s exact test) was used to examine the relation between qualitative variables. Survival analysis was done using the Kaplan–Meier method, and a comparison between two survival curves was done using the log‐rank test. A P value of <0.05 was considered significant.
We collected the following clinical data for each subject: age and sex, date of cSCC diagnoses, number of cSCCs, relapses, site of the neoplasm, number of surgical interventions needed for radical excision, use of dermal substitutes, and infections in the postoperative period.
Statistical analysis was performed with the STATA program, version 14 (StataCorp LP 4905 Lakeway Drive College Station, Texas 77,845 USA). Numerical data were expressed as mean and standard deviation or median and range as appropriate. Qualitative data were expressed as frequency and percentage. The chi‐square test (Fisher’s exact test) was used to examine the relation between qualitative variables. Survival analysis was done using the Kaplan–Meier method, and a comparison between two survival curves was done using the log‐rank test. A P value of <0.05 was considered significant.
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