Quantitative variables are described by mean and standard deviation or median and interquartile range based on whether they followed a normal distribution. To test the normal distribution, the Kolmogorov-Smirnov test was used. For comparisons among groups, Student's t test was used in the case of continuous variables and Chi-square in the case of qualitative variables, considering the value of P < .05 as statistically significant. We performed a logistic regression analysis to perform the univariate adjustment considering the clinical variables that have been demonstrated a relevant association with poor quality of anticoagulation (age, sex, hypertension, diabetes mellitus, previous bleeding, heart disease, Charlson index, CHADS 2 , CHA 2 DS 2 -VASc, HAS-BLED and specialist responsible of anticoagulation control). A multivariate logistic regression analysis was carried out with those of the relevant variables included in the univariate with a value of P < .150. The results are presented as odds ratio (OR) with a 95% confidence interval. STATA statistical version 12.0 was employed for the statistical analysis.
Stata statistical version 12
Manufactured by StataCorp
Sourced in United States
STATA statistical software version 12.0 is a comprehensive, integrated statistical software package developed by StataCorp. It provides a wide range of statistical analysis tools and data management capabilities for researchers, analysts, and professionals across various industries.
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Lab products found in correlation
5 protocols using stata statistical version 12
1
Anticoagulation Quality Predictors Analysis
2
Meta-analysis of Survival Outcomes
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Data on patients with 1-year OS, 1-year PFS, ORR, and adverse events were extracted from all of the included trials; risk ratio and 95% CI were calculated to assess the association strength of these two regimens with outcomes. The Q statistic and I2 statistic were used to assess the heterogeneity. I2 >50% indicated statistically significant heterogeneity. The random-effect model was used in these meta-analyses for conservative statistics. A funnel plot was used to assess the publication bias. We also performed sensitivity analysis of the trials included in our meta-analysis to examine whether the results of the meta-analysis were robust. Begg’s adjusted rank correlation test15 (link) and Egger’s regression test16 (link) were used to assess the funnel-plot asymmetric. A statistical test with a P<0.05 was considered significant. STATA statistical version 12.0 was used to perform all the statistical analyses (Stata Corporation, College Station, TX, USA). All P-values were two-sided.
3
Meta-analysis of Recurrence, Progression, and Adverse Events
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Data on patients with recurrence, progression, and events of adverse effects were extracted from all of the included trials, and the RR and 95% CIs were calculated to assess the strength of the association of these 2 drugs with risk of recurrence, progression, and adverse events. The heterogeneity test was assessed by the Q statistic and I2 statistic. I2>40% indicated statistically significant heterogeneity and that the statistical method should be changed to random-effect model. In other cases, a fixed-effect model was used. Analysis of subgroups was carried out according to clinical characteristics. Sensitivity analysis was performed to examine whether the findings in the meta-analysis were robust. Publication bias was investigated by a Begg modified funnel plot. An asymmetric plot suggested possible publication bias. Funnel-plot asymmetricity was assessed by the method of Begg adjusted rank correlation test12 (link) and Egger regression test.13 (link) A statistical test with a p-value <0.05 was considered significant. All statistical analyses were performed by using STATA statistical version 12.0 (Stata Corporation, College Station, TX, USA). All p-values were 2-sided.
4
Meta-analysis of Treatment Outcomes
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Data of patients with 1-year OS, 1-year PFS, ORR and AEs were extracted from all of the included trials. RR and 95%CI were calculated to assess the association strength of these two regimens with outcomes. The Q statistic and I2 statistic were used to assess the heterogeneity. I2>50% indicated statistically significant heterogeneity. The random-effect model was used in these meta-analyses for conservative statistics. A funnel plot was used to assess the publication bias. We also performed sensitivity analysis to examine whether the results of the meta-analysis were robust. Begg adjusted rank correlation test10 (link) and Egger regression test.11 (link) A statistical test with a p<0.05 was considered significant. STATA statistical version 12.0 was used to perform all the statistical analyses (Stata Corporation, College Station, Texas, USA). All p-values were two-sided.
5
Mortality Meta-analysis Utilizing Adjusted/Unadjusted ORs
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We pooled the adjusted OR or unadjusted OR with 95% confidence interval (95% CI) as the effect size of this meta-analysis. The data of mortality were extracted at two or more different time points. When the OR of all the studies was pooled, we selected the time point of 3 months initially, or else, the latest time point was selected. Since the latest time is proximal to 3 months, it can decrease the heterogeneity due to the different times of assessment. The heterogeneity was evaluated using I2 and P value based on Chi-square test. I2 ≤ 50% or P ≥ 0.1 did not demonstrate a significant heterogeneity. A fixed-effects model (Mantel–Haenszel method) was used. I2 > 50% or P < 0.1 indicated a significant heterogeneity, and therefore, a random-effects model (DerSimonian and Laird method) was applied. Moreover, the sensitivity analysis was utilized to identify the sources of heterogeneity and evaluate the stability of this meta-analysis. A funnel plot was drawn to assess the publication bias. Then, the Egger's method judged the publication bias when P < 0.05. STATA statistical version 12.0 (Stata Corporation, College Station, Texas, USA) was used for the data analyses.
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