All analyses were performed using Stata version 15.1 for Windows (Stata Corp., College Station, TX, USA). Quantitative variables were expressed as mean/median and Standard deviation/IQR, and qualitative variables were expressed as proportions (%). A p-value <5% was considered to be statistically significant. Data distribution was checked by Normal probability plot and Kolmogorov-Smirnov normality test. For the comparison of two groups, student’s t-test was used if following normal distribution, otherwise Mann Whitney U-test was used. Paired t-test was used to test the mean difference between two sets of observations. Intraclass correlation coefficients (ICC) were calculated to determine the agreement between POCT and venous blood values. Qualitative variables were compared between the two groups using Chi-square test or Fisher’s exact test. For the comparison of more than two groups One-way analysis of variance followed by Bonferroni correction for multiple comparison was applied. Pearson’s correlation coefficient between study variables were calculated along with the assessment for the significance of these correlations. Odds Ratios (95% CI) were calculated for study variables associated with outcome.
Stata version 15.1 for windows
Manufactured by StataCorp
Sourced in United States
Stata version 15.1 for Windows is a statistical software package designed for data analysis, management, and visualization. It provides a comprehensive set of tools for researchers, analysts, and professionals across various fields. The software supports a wide range of data formats and offers a variety of statistical methods, including regression analysis, time series analysis, and more. Stata version 15.1 for Windows is a powerful tool for data-driven decision-making and research.
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Lab products found in correlation
8 protocols using stata version 15.1 for windows
1
Comparison of POCT and Venous Blood
2
Survival Analysis of Breast Cancer Treatment
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The differences of demographic and clinic-pathological features between chemotherapy group and control group were analyzed by chisquare and Wilcoxon ranksum test. Propensity Score Matching (PSM) method (Match Ratio 1:1; Logit model; the nearest neighbor matching approach) was used to eliminate clinic-pathological mixed bias in two groups (Supplementary do le). Overall survival (OS) was de ned as the time from admission to the date of death from any cause. Breast cancer-speci c mortality (BCSM) was de ned as the period from the operative date to death of breast cancer. The OS curves and BCSM curves of each group were estimated by Kaplan-Meier survival analyses, and the curves were analyzed by the log-rank test. In the multivariate analysis, a COX's Proportional Hazard Model was employed to estimate whether a factor was a signi cant independent prognostic factor of survival. All statistical tests were two-sided, P values less than 0.05 were considered as statistically signi cant. The statistical analyses were performed using STATA version 15.1 for windows (StataCorp LLC).
3
Survival Analysis of Breast Cancer Patients
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The differences of demographic and clinic-pathological features between chemotherapy group and control group were analyzed by chi-square and Wilcoxon ranksum test. Propensity Score Matching (PSM) method (Match Ratio 1:1; Logit model; the nearest neighbor matching approach) was used to eliminate clinic-pathological mixed bias in two groups (Supplementary dofile). Overall survival (OS) was defined as the time from admission to the date of death from any cause. Breast cancer-specific mortality (BCSM) was defined as the period from the operative date to death of breast cancer. The OS curves and BCSM curves of each group were estimated by Kaplan-Meier survival analyses, and the curves were analyzed by the log-rank test. In the multivariate analysis, a COX's Proportional Hazard Model was employed to estimate whether a factor was a significant independent prognostic factor of survival. All statistical tests were two-sided, P values less than 0.05 were considered as statistically significant. The statistical analyses were performed using STATA version 15.1 for windows (StataCorp LLC).
4
Statistical Analysis of Biometric Factors
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Statistical analysis was performed using Stata version 15.1 for Windows (Stata Corp, College Station, TX, USA). Results are expressed as mean ± standard deviation or Pearson correlation coefficient for bivariate analysis and as multivariable‐adjusted mean ± standard error or standardized beta coefficient for multivariable analysis. Bivariate analysis was performed using chi‐square for categorical variables or Student's t test or Kruskal–Wallis test for continuous variables. Multivariate analysis was performed using analysis of variance or linear regression adjusted for age (continuous), BMI (continuous), C‐reactive protein (continuous), and hormonal therapy (yes/no). Results were considered significant if p < 0.05.
5
Corrected Iron Biomarkers Analysis
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Statistical analyses were performed using Stata version 15.1 for Windows (Stata Corp, College Station, TX, USA). Baseline characteristics of the study population are described as frequencies and percentages for categorical variables, mean and standard deviation, or median and 25th–75th percentile for continuous variables. The Gaussian distribution of continuous variables was visually inspected using a histogram and applying the Shapiro–Wilk test. Skewed variables (iron biomarkers, insulin, and hs-CRP) were log-transformed to achieve a normal distribution. Iron markers were corrected for CRP as suggested by others [18 (link), 19 (link)] prior to analysis. Two sets of analyses were performed: cross-sectional and longitudinal.
6
Dental Anxiety Prevalence and Consequences
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The statistical programme Stata (version 15.1) for Windows (Stata Corp, College Station, TX, USA) was used in these secondary data analyses. Survey data weights were used. After calculating DAS scores by summing the responses to the four items, we examined first the occurrence of dental anxiety and then the consequences of it. Cross-tabulations were used in describing its prevalence, and analysis of variance was used to examine the associations using the DAS scale as a continuous score (representing the ‘severity’ of dental anxiety). After examining the sociodemographic associations of dental anxiety (that is, the DAS score continuous variable) at the bivariate level, we modelled its prevalence using negative binomial regression. We then examined the clinical and dental service-use consequences of dental anxiety within three key age groups (18–34 years, 35–54 years and 55+ years). All estimates are presented with 95% CIs.
7
Survival Analysis of Chemotherapy Efficacy
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The differences of demographic and clinic-pathological features between chemotherapy group and control group were analyzed by chi-square and Wilcoxon ranksum test. Propensity Score Matching (PSM) method (Match Ratio 1:1; Logit model; the nearest neighbor matching approach) was used to eliminate clinic-pathological mixed bias in two groups (Supplementary dofile ). Overall survival (OS) was defined as the time from admission to the date of death from any cause. Breast cancer-specific mortality (BCSM) was defined as the period from the operative date to death of breast cancer. The OS curves and BCSM curves of each group were estimated by Kaplan-Meier survival analyses, and the curves were analyzed by the log-rank test. In the multivariate analysis, a COX’s Proportional Hazard Model was employed to estimate whether a factor was a significant independent prognostic factor of survival. All statistical tests were two-sided, P values less than 0.05 were considered as statistically significant. The statistical analyses were performed using STATA version 15.1 for windows (StataCorp LLC).
8
Probiotic Intervention in Health
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The data are shown as the mean ± standard error of the mean (SEM) for continuous outcomes or as an absolute number and percentage for categorical outcomes. A paired t-test was performed for normally distributed data. The Wilcoxon signed-rank test analyzed the skewed data. The differentiation of outcomes between the placebo and probiotic groups was compared using a Mann–Whitney U test. The p-value < 0.05 was set as significant (two-tailed). The STATA version 15.1 for Windows (StataCorp, College Station, TX, USA) was utilized for statistical analysis.
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